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Review Articles

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

, ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Pages 411-437 | Received 06 Nov 2017, Accepted 02 Mar 2018, Published online: 26 Mar 2018

References

  • Abdullah, S. S., Malek, M., Abdullah, N. S., & Mustapha, A. (2015). Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration. Sains Malaysiana, 44(7), 1053–1059. doi: 10.17576/jsm-2015-4407-18
  • Abghari, H., Ahmadi, H., Besharat, S., & Rezaverdinejad, V. (2012). Prediction of daily pan evaporation using wavelet neural networks. Water Resources Management, 26(12), 3639–3652. doi: 10.1007/s11269-012-0096-z
  • Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1–4), 28–40. doi: 10.1016/j.jhydrol.2011.06.013
  • Adamowski, J., & Sun, K. (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390(1–2), 85–91. doi: 10.1016/j.jhydrol.2010.06.033
  • Adib, A., & Mahmoodi, A. (2017). Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE Journal of Civil Engineering, 21(1), 447–457. doi: 10.1007/s12205-016-0444-2
  • Adriaenssens, V., De Baets, B., Goethals, P. L., & De Pauw, N. (2004). Fuzzy rule-based models for decision support in ecosystem management. Science of the Total Environment, 319(1-3), 1–12. doi: 10.1016/S0048-9697(03)00433-9
  • Afan, H. A., El-Shafie, A., Yaseen, Z. M., Hameed, M. M., Mohtar, W. H. M. W., & Hussain, A. (2015). ANN based sediment prediction model utilizing different input scenarios. Water Resources Management, 29(4), 1231–1245. doi: 10.1007/s11269-014-0870-1
  • Ahmad, S., & Simonovic, S. (2011). A three-dimensional fuzzy methodology for flood risk analysis. Journal of Flood Risk Management, 4(1), 53–74. doi: 10.1111/j.1753-318X.2011.01090.x
  • Ahmad, S., & Simonovic, S. P. (2006). An intelligent decision support system for management of floods. Water Resources Management, 20(3), 391–410. doi: 10.1007/s11269-006-0326-3
  • Altunkaynak, A. (2009). Sediment load prediction by genetic algorithms. Advances in Engineering Software, 40(9), 928–934. doi: 10.1016/j.advengsoft.2008.12.009
  • Alvisi, S., & Franchini, M. (2011). Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environmental Modelling & Software, 26(4), 523–537. doi: 10.1016/j.envsoft.2010.10.016
  • Aqil, M., Kita, I., Yano, A., & Soichi, N. (2006). Decision support system for flood crisis management using artificial neural network. International Journal of Intelligent Technology, 1(1), 70–76.
  • Ashrafi, M., Chua, L. H. C., Quek, C., & Qin, X. (2017). A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data. Journal of Hydrology, 545, 424–435. doi: 10.1016/j.jhydrol.2016.11.057
  • Astel, A. M., Walna, B., Simeonov, V., & Kurzyca, I. (2008). Multivariate statistics as means of tracking atmospheric pollution trends in western Poland. Journal of Environmental Science and Health, Part A, 43(3), 313–328. doi: 10.1080/10934520701792852
  • Aydogdu, M., & Firat, M. (2015). Estimation of failure rate in water distribution network using fuzzy clustering and LS-SVM methods. Water Resources Management, 29(5), 1575–1590. doi: 10.1007/s11269-014-0895-5
  • Bacanli, U. G., Firat, M., & Dikbas, F. (2009). Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23(8), 1143–1154. doi: 10.1007/s00477-008-0288-5
  • Badrzadeh, H., Sarukkalige, R., & Jayawardena, A. (2013). Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting. Journal of Hydrology, 507, 75–85. doi: 10.1016/j.jhydrol.2013.10.017
  • Badrzadeh, H., Sarukkalige, R., & Jayawardena, A. (2015). Hourly runoff forecasting for flood risk management: Application of various computational intelligence models. Journal of Hydrology, 529, 1633–1643. doi: 10.1016/j.jhydrol.2015.07.057
  • Bai, Y., Chen, Z., Xie, J., & Li, C. (2016). Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. Journal of Hydrology, 532, 193–206. doi: 10.1016/j.jhydrol.2015.11.011
  • Baliyan, A., Gaurav, K., & Mishra, S. K. (2015). A review of short term load forecasting using artificial neural network models. Procedia Computer Science, 48, 121–125. doi: 10.1016/j.procs.2015.04.160
  • Ball, J. E. (2014). Data mining for design flood prediction.
  • Baltar, A. M., & Fontane, D. G. (2008). Use of multiobjective particle swarm optimization in water resources management. Journal of Water Resources Planning and Management, 134(3), 257–265. doi: 10.1061/(ASCE)0733-9496(2008)134:3(257)
  • Bardossy, A., Bogardi, I., & Duckstein, L. (1991). Fuzzy set and probabilistic techniques for health-risk analysis. Applied Mathematics and Computation, 45(3), 241–268. doi: 10.1016/0096-3003(91)90083-Y
  • Baum, R. L., & Godt, J. W. (2010). Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides, 7(3), 259–272. doi: 10.1007/s10346-009-0177-0
  • Bazartseren, B., Hildebrandt, G., & Holz, K.-P. (2003). Short-term water level prediction using neural networks andneuro-fuzzy approach. Neurocomputing, 55(3-4), 439–450. doi: 10.1016/S0925-2312(03)00388-6
  • Belayneh, A., Adamowski, J., Khalil, B., & Ozga-Zielinski, B. (2014). Long-term SPI drought forecasting in the awash river basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418–429. doi: 10.1016/j.jhydrol.2013.10.052
  • Berenguer, M., Sempere-Torres, D., & Hürlimann, M. (2015). Debris-flow forecasting at regional scale by combining susceptibility mapping and radar rainfall. Natural Hazards and Earth System Sciences, 15(3), 587–602. doi: 10.5194/nhess-15-587-2015
  • Bezdek, J. (2013). Pattern recognition with fuzzy objective function algorithms. New York: Springer Science & Business Media.
  • Bolshakov, V. (2013). Regression-based Daugava river flood forecasting and monitoring. Information Technology and Management Science, 16(1), 137–142. doi: 10.2478/itms-2013-0021
  • Bovis, M. J., & Jakob, M. (1999). The role of debris supply conditions in predicting debris flow activity. Earth Surface Processes and Landforms, 24(11), 1039–1054. doi: 10.1002/(SICI)1096-9837(199910)24:11<1039::AID-ESP29>3.0.CO;2-U
  • Cannas, B., Fanni, A., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Physics and Chemistry of the Earth, Parts A/B/C, 31(18), 1164–1171. doi: 10.1016/j.pce.2006.03.020
  • Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd international conference on Machine learning.
  • Chang, Y. T., Chang, L. C., & Chang, F. J. (2005). Intelligent control for modeling of real-time reservoir operation, part II: Artificial neural network with operating rule curves. Hydrological Processes, 19(7), 1431–1444. doi: 10.1002/hyp.5582
  • Chang, T.-C., & Chao, R.-J. (2006). Application of back-propagation networks in debris flow prediction. Engineering Geology, 85(3-4), 270–280. doi: 10.1016/j.enggeo.2006.02.007
  • Chang, F.-J., Chen, P.-A., Lu, Y.-R., Huang, E., & Chang, K.-Y. (2014). Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. Journal of Hydrology, 517, 836–846. doi: 10.1016/j.jhydrol.2014.06.013
  • Chang, L.-C., Shen, H.-Y., & Chang, F.-J. (2014). Regional flood inundation nowcast using hybrid SOM and dynamic neural networks. Journal of Hydrology, 519, 476–489. doi: 10.1016/j.jhydrol.2014.07.036
  • Chattopadhyay, S., Pratihar, D. K., & Sarkar, S. C. D. (2012). A comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms. Computing and Informatics, 30(4), 701–720.
  • Chau, K.-w. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52(7), 726–733. doi: 10.1016/j.marpolbul.2006.04.003
  • Chau, K., Wu, C., & Li, Y. (2005). Comparison of several flood forecasting models in Yangtze river. Journal of Hydrologic Engineering, 10(6), 485–491. doi: 10.1061/(ASCE)1084-0699(2005)10:6(485)
  • Chen, C.-S., Chen, B. P.-T., Chou, F. N.-F., & Yang, C.-C. (2010). Development and application of a decision group back-propagation neural network for flood forecasting. Journal of Hydrology, 385(1-4), 173–182. doi: 10.1016/j.jhydrol.2010.02.019
  • Chevalier, G. G. (2013). Assessing debris-flow hazard focusing on statistical morpho-fluvial susceptibility models and magnitude-frequency relationships. Application to the central-eastern Pyrenees.
  • Chiang, P.-K., & Willems, P. (2014). Model predictive control combined with genetic algorithms for a river system.
  • Chidthong, Y., Tanaka, H., & Supharatid, S. (2009). Developing a hybrid multi-model for peak flood forecasting. Hydrological Processes, 23(12), 1725–1738. doi: 10.1002/hyp.7307
  • Chongfu, H. (1996). Fuzzy risk assessment of urban natural hazards. Fuzzy Sets and Systems, 83(2), 271–282. doi: 10.1016/0165-0114(95)00382-7
  • Cigizoglu, H. K. (2003). Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences Journal, 48(3), 349–361. doi: 10.1623/hysj.48.3.349.45288
  • Cigizoglu, H. K. (2005). Generalized regression neural network in monthly flow forecasting. Civil Engineering and Environmental Systems, 22(2), 71–81. doi: 10.1080/10286600500126256
  • Cigizoglu, H. K., & Kisi, Ö. (2006). Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology, 317(3-4), 221–238. doi: 10.1016/j.jhydrol.2005.05.019
  • Corani, G., & Guariso, G. (2005). Coupling fuzzy modeling and neural networks for river flood prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(3), 382–390. doi: 10.1109/TSMCC.2004.843229
  • Cordón, O., Herrera, F., Hoffmann, F., & Magdalena, L. (2001). Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. Singapore: World Scientific.
  • Curteanu, S., Dragoi, E.-N., & Dafinescu, V. (2015). Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling. International Work-Conference on Artificial Neural Networks.
  • Dai, C., Li, Y., & Huang, G. (2011). A two-stage support-vector-regression optimization model for municipal solid waste management–a case study of Beijing, China. Journal of Environmental Management, 92(12), 3023–3037. doi: 10.1016/j.jenvman.2011.06.038
  • Damle, C., & Yalcin, A. (2007). Flood prediction using time series data mining. Journal of Hydrology, 333(2–4), 305–316. doi: 10.1016/j.jhydrol.2006.09.001
  • Dehghani, M., Saghafian, B., Nasiri Saleh, F., Farokhnia, A., & Noori, R. (2014). Uncertainty analysis of streamflow drought forecast using artificial neural networks and monte-carlo simulation. International Journal of Climatology, 34(4), 1169–1180. doi: 10.1002/joc.3754
  • Dhanya, C., & Nagesh Kumar, D. (2009). Data mining for evolution of association rules for droughts and floods in India using climate inputs. Journal of Geophysical Research: Atmospheres, 114, D02102. doi:doi: 10.1029/2008JD010485
  • Di, Y., Ding, W., Mu, Y., Small, D. L., Islam, S., & Chang, N.-B. (2015). Developing machine learning tools for long-lead heavy precipitation prediction with multi-sensor data. Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on.
  • Dong, C., Wang, G., Chen, Z., & Yu, Z. (2008). A method of self-adaptive inertia weight for PSO. Computer Science and Software Engineering, 2008 International Conference on.
  • Drawid, A., & Gerstein, M. (2000). A Bayesian system integrating expression data with sequence patterns for localizing proteins: Comprehensive application to the yeast genome 1 1Edited by F. Cohen. Journal of Molecular Biology, 301(4), 1059–1075. doi: 10.1006/jmbi.2000.3968
  • Duncan, A., Tyrrell, D., Smart, N., Keedwell, E., Djordjevic, S., & Savic, D. (2013). Comparison of machine learning classifier models for bathing water quality exceedances in UK.
  • Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.
  • Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Boca Ratón: CRC press.
  • Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19(1), 43–53. doi: 10.1016/j.aei.2005.01.004
  • Eslamian, S., & Lavaei, N. (2009). Modelling nitrate pollution of groundwater using artificial neural network and genetic algorithm in an arid zone. International Journal of Water, 5(2), 194–203. doi: 10.1504/IJW.2009.028726
  • Esogbue, A. O. (1996). Fuzzy sets modeling and optimization for disaster control systems planning. Fuzzy Sets and Systems, 81(1), 169–183. doi: 10.1016/0165-0114(95)00248-0
  • Evrendilek, F. (2014). Assessing neural networks with wavelet denoising and regression models in predicting diel dynamics of eddy covariance-measured latent and sensible heat fluxes and evapotranspiration. Neural Computing and Applications, 24(2), 327–337. doi: 10.1007/s00521-012-1240-7
  • Faghih, M., Mirzaei, M., Adamowski, J., Lee, J., & El-Shafie, A. (2017). Uncertainty estimation in flood inundation mapping: An application of Non-parametric bootstrapping. River Research and Applications, 33(4), 611–619. doi: 10.1002/rra.3108
  • Faizollahzadeh_Ardabili, S., Mahmoudi, A., Gundoshmian, T. M., & Roshanianfard, A. (2016). Modeling and comparison of fuzzy and on/off controller in a mushroom growing hall. Measurement, 90, 127–134. doi: 10.1016/j.measurement.2016.04.050
  • Faizollahzadeh_Ardabili, S., Najafi, B., Ghaebi, H., Shamshirband, S., & Mostafaeipour, A. (2017). A novel enhanced exergy method in analyzing HVAC system using soft computing approaches: A case study on mushroom growing hall. Journal of Building Engineering, 13, 309–318. doi: 10.1016/j.jobe.2017.08.008
  • Fazel, S. A., Mirfenderesk, H., Blumenstein, M., & Tomlinson, R. (2014). Application of neural network to flood forecasting, an examination of model sensitivity to rainfall assumptions. Proceedings of the 7th International Congress on Environmental Modelling and Software, Ames, DP, Quinn, NWT, Rizzoli, AE (Eds.), San Diego, USA.
  • Fernando, A., Zhang, X., & Kinley, P. F. (2006). Combined sewer overflow forecasting with feed-forward back-propagation artificial neural network.
  • Fisher, B. E. (2006). Fuzzy approaches to environmental decisions: Application to air quality. Environmental Science & Policy, 9(1), 22–31. doi: 10.1016/j.envsci.2005.08.006
  • Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Intelligent decision making through a simulation of evolution. Systems Research and Behavioral Science, 11(4), 253–272. doi: 10.1002/bs.3830110403
  • Forcael, E., González, V., Orozco, F., Vargas, S., Pantoja, A., & Moscoso, P. (2014). Ant colony optimization model for tsunamis evacuation routes. Computer-Aided Civil and Infrastructure Engineering, 29(10), 723–737. doi: 10.1111/mice.12113
  • French, M. N., Krajewski, W. F., & Cuykendall, R. R. (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137(1-4), 1–31. doi: 10.1016/0022-1694(92)90046-X
  • Fu, G. (2008). A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation. Expert Systems with Applications, 34(1), 145–149. doi: 10.1016/j.eswa.2006.08.021
  • Ganguli, P., & Reddy, M. J. (2014). Ensemble prediction of regional droughts using climate inputs and the SVM– copulaapproach. Hydrological Processes, 28(19), 4989–5009. doi: 10.1002/hyp.9966
  • Gartner, J. E., Cannon, S. H., & Santi, P. M. (2014). Empirical models for predicting volumes of sediment deposited by debris flows and sediment-laden floods in the transverse ranges of southern California. Engineering Geology, 176, 45–56. doi: 10.1016/j.enggeo.2014.04.008
  • Guo, X., Hu, T., Wu, C., Zhang, T., & Lv, Y. (2013). Multi-objective optimization of the proposed multi-reservoir operating policy using improved NSPSO. Water Resources Management, 27(7), 2137–2153. doi: 10.1007/s11269-013-0280-9
  • Guven, A., & Kişi, Ö. (2011). Daily pan evaporation modeling using linear genetic programming technique. Irrigation Science, 29(2), 135–145. doi: 10.1007/s00271-010-0225-5
  • Hall, J. W., Manning, L. J., & Hankin, R. K. (2011). Bayesian calibration of a flood inundation model using spatial data. Water Resources Research, 47, W05529. doi:doi: 10.1029/2009WR008541
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Waltham: Elsevier.
  • He, L., Huang, G., & Lu, H. (2008). A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty. Advances in Water Resources, 31(12), 1622–1635. doi: 10.1016/j.advwatres.2008.07.009
  • He, Y., Xu, Q., Yang, S., & Liao, L. (2014). Reservoir flood control operation based on chaotic particle swarm optimization algorithm. Applied Mathematical Modelling, 38(17-18), 4480–4492. doi: 10.1016/j.apm.2014.02.030
  • He, Y., Zhou, J., Kou, P., Lu, N., & Zou, Q. (2011). A fuzzy clustering iterative model using chaotic differential evolution algorithm for evaluating flood disaster. Expert Systems with Applications, 38(8), 10060–10065. doi: 10.1016/j.eswa.2011.02.003
  • Herbst, M., Casper, M., Grundmann, J., & Buchholz, O. (2009). Comparative analysis of model behaviour for flood prediction purposes using self-organizing maps. Natural Hazards and Earth System Science, 9(2), 373–392. doi: 10.5194/nhess-9-373-2009
  • Hmaidi, K., & Akaichi, J. (2014). Floods trajectories modeling and dynamic relief planning: A bees foraging approach. International Conference on Swarm Intelligence Based Optimization.
  • Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: MIT press.
  • Holz, K.-P., Hildebrandt, G., & Weber, L. (2006). Concept for a web-based information system for flood management. Natural Hazards, 38(1-2), 121–140. doi: 10.1007/s11069-005-8605-5
  • Hsiao, T.-C. R., Lin, C.-W., & Chiang, H. K. (2003). Partial least-squares algorithm for weights initialization of backpropagation network. Neurocomputing, 50, 237–247. doi: 10.1016/S0925-2312(01)00708-1
  • Hsu, K. l., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517–2530. doi: 10.1029/95WR01955
  • Huang, Z., Zhou, J., Song, L., Lu, Y., & Zhang, Y. (2010). Flood disaster loss comprehensive evaluation model based on optimization support vector machine. Expert Systems with Applications, 37(5), 3810–3814. doi: 10.1016/j.eswa.2009.11.039
  • Jain, S., Das, A., & Srivastava, D. (1999). Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, 125(5), 263–271. doi: 10.1061/(ASCE)0733-9496(1999)125:5(263)
  • Jain, A., & Satish, B. (2009). Clustering based short term load forecasting using support vector machines. PowerTech, 2009 IEEE Bucharest.
  • Jiang, W., Deng, L., Chen, L., Wu, J., & Li, J. (2009). Risk assessment and validation of flood disaster based on fuzzy mathematics. Progress in Natural Science, 19(10), 1419–1425. doi: 10.1016/j.pnsc.2008.12.010
  • Jinxing, Z., Lixian, W., Baoyuan, X., Shimin, F., & Xilin, W. (2002). A study on the early—warning technique concerning debris flow disasters. Journal of Geographical Sciences, 12(3), 363–370. doi: 10.1007/BF02837558
  • Kalayathankal, S. J., & Singh, G. S. (2010). A fuzzy soft flood alarm model. Mathematics and Computers in Simulation, 80(5), 887–893. doi: 10.1016/j.matcom.2009.10.003
  • Kant, A., Suman, P. K., Giri, B. K., Tiwari, M. K., Chatterjee, C., Nayak, P. C., & Kumar, S. (2013). Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap-based neural network for flood forecasting. Neural Computing and Applications, 23(1), 231–246. doi: 10.1007/s00521-013-1344-8
  • Karbowski, A., Malinowski, K., & Niewiadomska-Szynkiewicz, E. (2005). A hybrid analytic/rule-based approach to reservoir system management during flood. Decision Support Systems, 38(4), 599–610. doi: 10.1016/j.dss.2003.10.001
  • Kasiviswanathan, K., He, J., Sudheer, K., & Tay, J.-H. (2016). Potential application of wavelet neural network ensemble to forecast streamflow for flood management. Journal of Hydrology, 536, 161–173. doi: 10.1016/j.jhydrol.2016.02.044
  • Ketabchi, H., & Ataie-Ashtiani, B. (2015). Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges. Journal of Hydrology, 520, 193–213. doi: 10.1016/j.jhydrol.2014.11.043
  • Khondoker, M., Dobson, R., Skirrow, C., Simmons, A., & Stahl, D. (2016). A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Statistical Methods in Medical Research, 25(5), 1804–1823. doi: 10.1177/0962280213502437
  • Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor river basin, Malaysia. Environmental Earth Sciences, 67(1), 251–264. doi: 10.1007/s12665-011-1504-z
  • Kim, T.-W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319–328. doi: 10.1061/(ASCE)1084-0699(2003)8:6(319)
  • Kisi, O. (2008). The potential of different ANN techniques in evapotranspiration modelling. Hydrological Processes, 22(14), 2449–2460. doi: 10.1002/hyp.6837
  • Kisi, O. (2015). Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water Resources Management, 29(14), 5109–5127. doi: 10.1007/s11269-015-1107-7
  • Kişi, Ö. (2009). Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering, 14(8), 773–782. doi: 10.1061/(ASCE)HE.1943-5584.0000053
  • Kisi, O., Dailr, A. H., Cimen, M., & Shiri, J. (2012). Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology, 450–451, 48–58. doi: 10.1016/j.jhydrol.2012.05.031
  • Kisi, O., Karimi, S., Shiri, J., Makarynskyy, O., & Yoon, H. (2014). Forecasting sea water levels at Mukho station, South Korea using soft computing techniques. The International Journal of Ocean and Climate Systems, 5(4), 175–188. doi: 10.1260/1759-3131.5.4.175
  • Kisi, O., Shiri, J., Karimi, S., Shamshirband, S., Motamedi, S., Petković, D., & Hashim, R. (2015). A survey of water level fluctuation predicting in urmia lake using support vector machine with firefly algorithm. Applied Mathematics and Computation, 270, 731–743. doi: 10.1016/j.amc.2015.08.085
  • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. doi: 10.1007/BF00337288
  • Koza, J. R. (1992). Genetic programming II, automatic discovery of reusable subprograms. Cambridge, MA: MIT Press.
  • Kreinovich, V., Nguyen, H. T., & Yam, Y. (1999). Fuzzy systems are universal approximators for a smooth function and its derivatives.
  • Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory. Biometrical Journal, 56(4), 534–563. doi: 10.1002/bimj.201300068
  • Kumar, M., Raghuwanshi, N., Singh, R., Wallender, W., & Pruitt, W. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), 224–233. doi: 10.1061/(ASCE)0733-9437(2002)128:4(224)
  • Kumar, D. N., & Reddy, M. J. (2006). Ant colony optimization for multi-purpose reservoir operation. Water Resources Management, 20(6), 879–898. doi: 10.1007/s11269-005-9012-0
  • Lafdani, E. K., Nia, A. M., & Ahmadi, A. (2013). Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology, 478, 50–62. doi: 10.1016/j.jhydrol.2012.11.048
  • Lai, C., Chen, X., Chen, X., Wang, Z., Wu, X., & Zhao, S. (2015). A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Natural Hazards, 77(2), 1243–1259. doi: 10.1007/s11069-015-1645-6
  • Lee, L.-W., & Chen, S.-M. (2008). Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. Expert Systems with Applications, 34(4), 2763–2771. doi: 10.1016/j.eswa.2007.05.009
  • Lee, C.-J., & Lee, K. J. (2006). Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal. Reliability Engineering & System Safety, 91(5), 515–532. doi: 10.1016/j.ress.2005.03.011
  • Leon, A. S., Kanashiro, E. A., Valverde, R., & Sridhar, V. (2014). Dynamic framework for intelligent control of river flooding: Case study. Journal of Water Resources Planning and Management, 140(2), 258–268. doi: 10.1061/(ASCE)WR.1943-5452.0000260
  • Li, Q. (2013). Fuzzy approach to analysis of flood risk based on variable fuzzy sets and improved information diffusion methods. Natural Hazards and Earth System Science, 13(2), 239–249. doi: 10.5194/nhess-13-239-2013
  • Li, Q., Zhou, J., Liu, D., & Jiang, X. (2012). Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion. Safety Science, 50(5), 1275–1283. doi: 10.1016/j.ssci.2012.01.007
  • Lin, J.-W., Chen, C.-W., & Peng, C.-Y. (2012). Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Natural Hazards, 64(1), 273–282. doi: 10.1007/s11069-012-0236-z
  • Lin, G.-F., Wang, T.-C., & Chen, L.-H. (2016). A forecasting approach combining self-organizing map with support vector regression for reservoir inflow during typhoon periods. Advances in Meteorology, 2016, Article ID 7575126. doi: 10.1155/2016/7575126.
  • Liu, M., & Lu, J. (2014). Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river? Environmental Science and Pollution Research, 21(18), 11036–11053. doi: 10.1007/s11356-014-3046-x
  • Liu, Y., & Pender, G. (2015). A flood inundation modelling using v-support vector machine regression model. Engineering Applications of Artificial Intelligence, 46, 223–231. doi: 10.1016/j.engappai.2015.09.014
  • Liu, Q.-J., Shi, Z.-H., Fang, N.-F., Zhu, H.-D., & Ai, L. (2013). Modeling the daily suspended sediment concentration in a hyperconcentrated river on the loess plateau, China, using the wavelet–ANN approach. Geomorphology, 186, 181–190. doi: 10.1016/j.geomorph.2013.01.012
  • Lobbrecht, A. H., & Solomatine, D. P. (2002). Machine learning in real-time control of water systems. Urban Water, 4(3), 283–289. doi: 10.1016/S1462-0758(02)00023-7
  • Lohani, A. K., Goel, N., & Bhatia, K. (2014). Improving real time flood forecasting using fuzzy inference system. Journal of Hydrology, 509, 25–41. doi: 10.1016/j.jhydrol.2013.11.021
  • Londhe, S. N., & Dixit, P. R. (2012). Genetic programming: a novel computing approach in modeling water flows Genetic Programming-New Approaches and Successful Applications: InTech.
  • Luo, J., Qi, Y., Xie, J., & Zhang, X. (2015). A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation. Applied Soft Computing, 34, 526–538. doi: 10.1016/j.asoc.2015.05.036
  • Marée, R., Geurts, P., Visimberga, G., Piater, J., & Wehenkel, L. (2004). A comparison of generic machine learning algorithms for image classification. In Research and development in intelligent systems XX (pp. 169–182). Springer.
  • Mendel, J. M. (1995). Fuzzy logic systems for engineering: A tutorial. Proceedings of the IEEE, 83(3), 345–377. doi: 10.1109/5.364485
  • Merz, R., & Blöschl, G. (2008a). Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information. Water Resources Research, 44(8), W08432. doi:10.1029/2007WR006744
  • Merz, R., & Blöschl, G. (2008b). Flood frequency hydrology: 2. Combining data evidence. Water Resources Research, 44(8), W08433. doi: 10.1029/2007WR006745
  • Merz, B., Kreibich, H., & Lall, U. (2013). Multi-variate flood damage assessment: A tree-based data-mining approach. Natural Hazards and Earth System Science, 13(1), 53–64. doi: 10.5194/nhess-13-53-2013
  • Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article” assessment of economic flood damage”. Natural Hazards and Earth System Science, 10(8), 1697–1724. doi: 10.5194/nhess-10-1697-2010
  • Minglei, R., Bende, W., Liang, Q., & Guangtao, F. (2010). Classified real-time flood forecasting by coupling fuzzy clustering and neural network. International Journal of Sediment Research, 25(2), 134–148. doi: 10.1016/S1001-6279(10)60033-9
  • Mishra, A., Desai, V., & Singh, V. (2007). Drought forecasting using a hybrid stochastic and neural network model. Journal of Hydrologic Engineering, 12(6), 626–638. doi: 10.1061/(ASCE)1084-0699(2007)12:6(626)
  • Misra, K. B., & Weber, G. G. (1990). Use of fuzzy set theory for level-I studies in probabilistic risk assessment. Fuzzy Sets and Systems, 37(2), 139–160. doi: 10.1016/0165-0114(90)90038-8
  • Mohanty, S., Jha, M. K., Kumar, A., & Panda, D. (2013). Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua inter-basin of Odisha, India. Journal of Hydrology, 495, 38–51. doi: 10.1016/j.jhydrol.2013.04.041
  • Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle swarm optimization applied to the design of water supply systems. Computers & Mathematics with Applications, 56(3), 769–776. doi: 10.1016/j.camwa.2008.02.006
  • Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water distribution system computer-aided design by agent swarm optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433–448. doi: 10.1111/mice.12062
  • Moosavi, V., Vafakhah, M., Shirmohammadi, B., & Behnia, N. (2013). A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resources Management, 27(5), 1301–1321. doi: 10.1007/s11269-012-0239-2
  • Mwale, F., Adeloye, A., & Rustum, R. (2014). Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: The case of the lower Shire floodplain, Malawi. Hydrology Research, 45(6), 838–854. doi: 10.2166/nh.2014.168
  • Myerson, R. B. (2013). Game theory. Cambridge: Harvard university press.
  • Nagy, H., Watanabe, K., & Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering, 128(6), 588–595. doi: 10.1061/(ASCE)0733-9429(2002)128:6(588)
  • Najah, A. A., El-Shafie, A., Karim, O. A., & Jaafar, O. (2012). Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Neural Computing and Applications, 21(5), 833–841. doi: 10.1007/s00521-010-0486-1
  • Napolitano, G., See, L., Calvo, B., Savi, F., & Heppenstall, A. (2010). A conceptual and neural network model for real-time flood forecasting of the tiber river in Rome. Physics and Chemistry of the Earth, Parts A/B/C, 35(3-5), 187–194. doi: 10.1016/j.pce.2009.12.004
  • Nayak, P., Sudheer, K., & Ramasastri, K. (2005). Fuzzy computing based rainfall–runoff model for real time flood forecasting. Hydrological Processes, 19(4), 955–968. doi: 10.1002/hyp.5553
  • Nkoana, R. (2011). Artificial neural network modelling of flood prediction and early warning. (Master Degree). University of the Free State, Bloemfontein Google Scholar.
  • Nourani, V., & Andalib, G. (2015). Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. Journal of Mountain Science, 12(1), 85–100. doi: 10.1007/s11629-014-3121-2
  • Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: A review. Journal of Hydrology, 514, 358–377. doi: 10.1016/j.jhydrol.2014.03.057
  • Nourani, V., Komasi, M., & Alami, M. T. (2012). Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff process. Journal of Hydrologic Engineering, 17(6), 724–741. doi: 10.1061/(ASCE)HE.1943-5584.0000506
  • Osanai, N., Shimizu, T., Kuramoto, K., Kojima, S., & Noro, T. (2010). Japanese early-warning for debris flows and slope failures using rainfall indices with radial basis function network. Landslides, 7(3), 325–338. doi: 10.1007/s10346-010-0229-5
  • Pappenberger, F., Frodsham, K., Beven, K., Romanowicz, R., & Matgen, P. (2007). Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations. Hydrology and Earth System Sciences Discussions, 11(2), 739–752. doi: 10.5194/hess-11-739-2007
  • Partal, T., & Cigizoglu, H. K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. Journal of Hydrology, 358(3-4), 317–331. doi: 10.1016/j.jhydrol.2008.06.013
  • Partal, T., & Kişi, Ö. (2007). Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342(1-2), 199–212. doi: 10.1016/j.jhydrol.2007.05.026
  • Patcha, A., & Park, J.-M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448–3470. doi: 10.1016/j.comnet.2007.02.001
  • Patki, V. K., Shrihari, S., Manu, B., & Deka, P. C. (2015). Fuzzy system modeling for forecasting water quality index in municipal distribution system. Urban Water Journal, 12(2), 89–110. doi: 10.1080/1573062X.2013.820333
  • PielkeJr, R. A., & Downton, M. W. (2000). Precipitation and damaging floods: Trends in the United States, 1932–97. Journal of Climate, 13(20), 3625–3637. doi: 10.1175/1520-0442(2000)013<3625:PADFTI>2.0.CO;2
  • Pyayt, A. L., Mokhov, I. I., Lang, B., Krzhizhanovskaya, V. V., & Meijer, R. J. (2011). Machine learning methods for environmental monitoring and flood protection. World Academy of Science, Engineering and Technology, 78, 118–123.
  • Qin, H., Zhou, J., Lu, Y., Li, Y., & Zhang, Y. (2010). Multi-objective cultured differential evolution for generating optimal trade-offs in reservoir flood control operation. Water Resources Management, 24(11), 2611–2632. doi: 10.1007/s11269-009-9570-7
  • Rahim, A., & Akif, A. (2015). Optimal artificial neural network modeling of sedimentation yield and runoff in high flow season of Indus river at Besham Qila for Terbela dam. Int J Sci Res, 4, 479–483.
  • Reddy, M. J., & Kumar, D. N. (2006). Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resources Management, 20(6), 861–878. doi: 10.1007/s11269-005-9011-1
  • Reddy, M. J., & Kumar, D. N. (2007). Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization. Hydrological Sciences Journal, 52(4), 686–701. doi: 10.1623/hysj.52.4.686
  • Reddy, M. J., & Nagesh Kumar, D. (2007). Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrological Processes, 21(21), 2897–2909. doi: 10.1002/hyp.6507
  • Reyna, V. F., & Brainerd, C. J. (2008). Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learning and Individual Differences, 18(1), 89–107. doi: 10.1016/j.lindif.2007.03.011
  • Rezoug, M., Meouche, R., Hamzaoui, R., & Feng, Z.-Q. (2013). Using the fast multi-objective genetic algorithm to improve the urban flood modeling. International Journal of Engineering and Technology, 5(3), 341–344. doi: 10.7763/IJET.2013.V5.571
  • Rogers, L. L., & Dowla, F. U. (1994). Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research, 30(2), 457–481. doi: 10.1029/93WR01494
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533–536. doi: 10.1038/323533a0
  • Sabzi, H. Z., Humberson, D., Abudu, S., & King, J. P. (2016). Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, southern New Mexico. Expert Systems with Applications, 43, 154–164. doi: 10.1016/j.eswa.2015.08.043
  • Sahay, R. R., & Srivastava, A. (2014). Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resources Management, 28(2), 301–317. doi: 10.1007/s11269-013-0446-5
  • Sättele, M., Bründl, M., & Straub, D. (2015). Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning. Reliability Engineering & System Safety, 142, 192–202. doi: 10.1016/j.ress.2015.05.003
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. doi: 10.1016/j.neunet.2014.09.003
  • Schnebele, E. (2013). Improving remote sensing flood assessment using volunteered geographical data. Natural Hazards and Earth System Sciences, 13(3), 669–677. doi: 10.5194/nhess-13-669-2013
  • Schwefel, H.-P. (1981). Numerical optimization of computer models. New York: John Wiley & Sons, Inc.
  • Seal, V., Raha, A., Maity, S., Mitra, S. K., Mukherjee, A., & Naskar, M. K. (2012). A real time multivariate robust regression based flood prediction model using polynomial approximation for wireless sensor network based flood forecasting systems. International Conference on Computer Science and Information Technology.
  • See, L., & Openshaw, S. (1999). Applying soft computing approaches to river level forecasting. Hydrological Sciences Journal, 44(5), 763–778. doi: 10.1080/02626669909492272
  • Sehgal, V., Sahay, R. R., & Chatterjee, C. (2014). Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resources Management, 28(6), 1733–1749. doi: 10.1007/s11269-014-0584-4
  • Sertel, E., Cigizoglu, H., & Sanli, D. (2008). Estimating daily mean sea level heights using artificial neural networks. Journal of Coastal Research, 243, 727–734. doi: 10.2112/06-742.1
  • Shin, H.-S., & Salas, J. D. (2000). Regional drought analysis based on neural networks. Journal of Hydrologic Engineering, 5(2), 145–155. doi: 10.1061/(ASCE)1084-0699(2000)5:2(145)
  • Shiri, J., Dierickx, W., Baba, A. P.-A., Neamati, S., & Ghorbani, M. (2011). Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 42(6), 491–502. doi: 10.2166/nh.2011.020
  • Shiri, J., Nazemi, A. H., Sadraddini, A. A., Landeras, G., Kisi, O., Fard, A. F., & Marti, P. (2013). Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration. Journal of Hydrology, 480, 46–57. doi: 10.1016/j.jhydrol.2012.12.006
  • Shouyu, C., & Yu, G. (2006). Variable fuzzy sets and its application in comprehensive risk evaluation for flood-control engineering system. Fuzzy Optimization and Decision Making, 5(2), 153–162. doi: 10.1007/s10700-006-7333-y
  • Singh, G., Kandasamy, J., Shon, H., & Cho, J. (2011). Measuring treatment effectiveness of urban wetland using hybrid water quality—artificial neural network (ANN) model. Desalination and Water Treatment, 32(1–3), 284–290. doi: 10.5004/dwt.2011.2712
  • Sivapragasam, C., Maheswaran, R., & Venkatesh, V. (2008). Genetic programming approach for flood routing in natural channels. Hydrological Processes, 22(5), 623–628. doi: 10.1002/hyp.6628
  • Solomatine, D. P., & Price, R. K. (2004). Innovative approaches to flood forecasting using data driven and hybrid modelling. In Hydroinformatics: (In 2 Volumes, with CD-ROM) (pp. 1639–1646). Singapore: World Scientific.
  • Song, S., & Singh, V. P. (2010). Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stochastic Environmental Research and Risk Assessment, 24(5), 783–805. doi: 10.1007/s00477-010-0364-5
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. doi: 10.1023/A:1008202821328
  • Sudheer, K., & Jain, A. (2004). Explaining the internal behaviour of artificial neural network river flow models. Hydrological Processes, 18(4), 833–844. doi: 10.1002/hyp.5517
  • Tabari, H., Martinez, C., Ezani, A., & Talaee, P. H. (2013). Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration. Irrigation Science, 31(4), 575–588. doi: 10.1007/s00271-012-0332-6
  • Tahmasebi, P., & Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences, 42, 18–27. doi: 10.1016/j.cageo.2012.02.004
  • Tayfur, G., Moramarco, T., & Singh, V. P. (2007). Predicting and forecasting flow discharge at sites receiving significant lateral inflow. Hydrological Processes, 21(14), 1848–1859. doi: 10.1002/hyp.6320
  • Tayyab, M., Zhou, J., Zeng, X., & Adnan, R. (2016). Discharge forecasting by applying artificial neural networks at the Jinsha river basin, China. European Scientific Journal, ESJ, 12(9), 108–127.
  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79. doi: 10.1016/j.jhydrol.2013.09.034
  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. doi: 10.1016/j.jhydrol.2014.03.008
  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 29(4), 1149–1165. doi: 10.1007/s00477-015-1021-9
  • Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101. doi: 10.1016/j.catena.2014.10.017
  • Tezel, G., & Buyukyildiz, M. (2016). Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and Applied Climatology, 124(1-2), 69–80. doi: 10.1007/s00704-015-1392-3
  • Tiwari, M. K., & Chatterjee, C. (2010). Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). Journal of Hydrology, 382(1-4), 20–33. doi: 10.1016/j.jhydrol.2009.12.013
  • Turan, M. E., & Yurdusev, M. A. (2014). Predicting monthly river flows by genetic fuzzy systems. Water Resources Management, 28(13), 4685–4697. doi: 10.1007/s11269-014-0767-z
  • Viglione, A., Merz, R., Salinas, J. L., & Blöschl, G. (2013). Flood frequency hydrology: 3. A Bayesian analysis. Water Resources Research, 49(2), 675–692. doi: 10.1029/2011WR010782
  • Vogel, K., Riggelsen, C., Merz, B., Kreibich, H., & Scherbaum, F. (2012). Flood damage and influencing factors: A Bayesian network perspective. 6th European workshop on probabilistic graphical models (PGM 2012), University of Granada, Granada, Spain.
  • Wang, S., Huang, G., & Baetz, B. W. (2015). An inexact probabilistic–possibilistic optimization framework for flood management in a hybrid uncertain environment. IEEE Transactions on Fuzzy Systems, 23(4), 897–908. doi: 10.1109/TFUZZ.2014.2333094
  • Wang, S., Huang, G. H., & Yang, B. (2012). An interval-valued fuzzy-stochastic programming approach and its application to municipal solid waste management. Environmental Modelling & Software, 29(1), 24–36. doi: 10.1016/j.envsoft.2011.10.007
  • Wang, J., Liu, Y., & Liu, Z. (2013). Spatio-temporal patterns of cropland conversion in response to the “grain for green project” in China’s loess hilly region of Yanchuan county. Remote Sensing, 5(11), 5642–5661. doi: 10.3390/rs5115642
  • Warmerdam, J. M., & Jacobs, T. L. (1994). Fuzzy set approach to routing and siting hazardous waste operations. Information Sciences-Applications, 2(1), 1–14. doi: 10.1016/1069-0115(94)90002-7
  • Wei, C.-C. (2012). Wavelet kernel support vector machines forecasting techniques: Case study on water-level predictions during typhoons. Expert Systems with Applications, 39(5), 5189–5199. doi: 10.1016/j.eswa.2011.11.020
  • Weyns, D., Parunak, H. V. D., Michel, F., Holvoet, T., & Ferber, J. (2004). Environments for multiagent systems state-of-the-art and research challenges. International Workshop on Environments for Multi-Agent Systems.
  • Wieprecht, S., Tolossa, H. G., & Yang, C. T. (2013). A neuro-fuzzy-based modelling approach for sediment transport computation. Hydrological Sciences Journal, 58(3), 587–599. doi: 10.1080/02626667.2012.755264
  • Woodward, M., Gouldby, B., Kapelan, Z., & Hames, D. (2014). Multiobjective optimization for improved management of flood risk. Journal of Water Resources Planning and Management, 140(2), 201–215. doi: 10.1061/(ASCE)WR.1943-5452.0000295
  • Wu, C., & Chau, K.-W. (2006). Evaluation of several algorithms in forecasting flood. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems.
  • Wu, C., Chau, K., & Li, Y. (2009). Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resources Research, 45(8), 111–116. doi: 10.1029/2007WR006737
  • Wu, M.-C., & Lin, G.-F. (2015). An hourly streamflow forecasting model coupled with an enforced learning strategy. Water, 7(11), 5876–5895. doi: 10.3390/w7115876
  • Yang, K., & Zhao, L. (2009). Load forecasting model based on amendment of mamdani fuzzy system. Wireless Communications, Networking and Mobile Computing, 2009. WiCom’09. 5th International Conference on.
  • Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829–844. doi: 10.1016/j.jhydrol.2015.10.038
  • Yazdi, J., & Neyshabouri, S. S. (2014). Adaptive surrogate modeling for optimization of flood control detention dams. Environmental Modelling & Software, 61, 106–120. doi: 10.1016/j.envsoft.2014.07.007
  • Yeh, C.-F., Wang, J., Yeh, H.-F., & Lee, C.-H. (2015). Spatial and temporal streamflow trends in northern Taiwan. Water, 7(2), 634–651. doi: 10.3390/w7020634
  • Yonaba, H., Anctil, F., & Fortin, V. (2010). Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. Journal of Hydrologic Engineering, 15(4), 275–283. doi: 10.1061/(ASCE)HE.1943-5584.0000188
  • Yu, P.-S., Chen, S.-T., & Chang, I.-F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704–716. doi: 10.1016/j.jhydrol.2006.01.021
  • Zadeh, L. A. (1994). Soft computing and fuzzy logic. IEEE Software, 11(6), 48–56. doi: 10.1109/52.329401
  • Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100, 9–34. doi: 10.1016/S0165-0114(99)80004-9
  • Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214(1-4), 32–48. doi: 10.1016/S0022-1694(98)00242-X
  • Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40. doi: 10.1109/MGRS.2016.2540798
  • Zhou, J., Coatrieux, J.-L., Bousse, A., Shu, H., & Luo, L. (2007). A Bayesian MAP-EM algorithm for PET image reconstruction using wavelet transform. IEEE Transactions on Nuclear Science, 54(5), 1660–1669. doi: 10.1109/TNS.2007.901200
  • Zhou, J., & Cui, M. (2008). Neural network-based early warning system for debris flow disaster in the Three Gorges Reservoir region. Natural Computation, 2008. ICNC’08. Fourth International Conference on.
  • Zou, Q., Zhou, J., Zhou, C., Guo, J., Deng, W., Yang, M., & Liao, L. (2012). Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model. Expert Systems with Applications, 39(6), 6213–6220. doi: 10.1016/j.eswa.2011.12.008
  • Zurada, J. M. (1992). Introduction to artificial neural systems. West St. Paul: West Publishing Co.