Comparison between NARX-NN and HEC-HMS models to simulate Wadi Seghir catchment runoff events in Algerian northern

References

• Abou Rjeily, Y., Abbas, O., Sadek, M., Shahrour, I., & Hage Chehade, F. (2017). Flood forecasting within urban drainage systems using NARX neural network. Water Science and Technology, 76(9), 2401–2412. https://doi.org/10.2166/wst.2017.409
   PubMed Web of Science ®Google Scholar
• Abushandi, E., & Merkel, B. (2013). Modelling rainfall runoff relations using HEC-HMS and IHACRES for a single rain event in an arid region of Jordan. Water Resources Management, 27(7), 2391–2409. https://doi.org/10.1007/s11269-013-0293-4
   Web of Science ®Google Scholar
• Ali, A. (2009). Nonlinear multivariate rainfall–stage model for large wetland systems. Journal of Hydrology, 374(3-4), 338–350. https://doi.org/10.1016/j.jhydrol.2009.06.033
   Web of Science ®Google Scholar
• Anthony, W. M., & Michael, J. H. (2004). Rainfall-runoff modelling. In J. A. Robert, E. K. Pauline, & S. Linda (Eds.), Neural networks for hydrological modelling. 1st ed. (pp. 157–175). CRC Press.
   Google Scholar
• Beal, M. H., Hagan, M. T., & Demuth, H. B. (2017). Neural Network Toolbox™ User’s Guide R2017b. The MathWorks.
   Google Scholar
• Bhattacharjee, N. V., & Tollner, E. W. (2016). Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network. Ecological Modelling, 339, 68–76. https://doi.org/10.1016/j.ecolmodel.2016.08.011
   Web of Science ®Google Scholar
• Bowden, G. J., Dandy, G. C., & Maier, H. R. (2005). Input determination for neural network models in water resources applications. Part 1—background and methodology. Journal of Hydrology, 301(1-4), 75–92. https://doi.org/10.1016/j.jhydrol.2004.06.021
   Web of Science ®Google Scholar
• Chang, F.-J., Chen, P.-A., Lu, Y.-R., Huang, E., & Chang, K.-Y. (2014a). Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. Journal of Hydrology, 517, 836–846. https://doi.org/10.1016/j.jhydrol.2014.06.013
   Web of Science ®Google Scholar
• Chang, F.-J., Tsai, Y.-H., Chen, P.-A., Coynel, A., & Vachaud, G. (2015). Modeling water quality in an urban river using hydrological factors – data driven approaches. Journal of Environmental Management, 151, 87–96. https://doi.org/10.1016/j.jenvman.2014.12.014
   PubMed Web of Science ®Google Scholar
• Chang, L.-C., Shen, H.-Y., & Chang, F.-J. (2014b). Regional flood inundation nowcast using hybrid SOM and dynamic neural networks. Journal of Hydrology, 519, 476–489. https://doi.org/10.1016/j.jhydrol.2014.07.036
   Web of Science ®Google Scholar
• Cheng, K.-S., Lien, Y.-T., Wu, Y.-C., & Su, Y.-F. (2017). On the criteria of model performance evaluation for real-time flood forecasting. Stochastic Environmental Research and Risk Assessment, 31(5), 1123–1146. https://doi.org/10.1007/s00477-016-1322-7
   Web of Science ®Google Scholar
• Christian, W. D., & Robert, L. W. (2004). Single network modelling solutions. In J. A. Robert, E. K. Pauline, & S. Linda (Eds.), Neural networks for hydrological modelling. 1st ed. (pp. 38–58) CRC Press.
   Google Scholar
• Chu, X., & Steinman, A. (2009). Event and continuous hydrologic modeling with HEC-HMS. Journal of Irrigation and Drainage Engineering, 135(1), 119–124.
   Web of Science ®Google Scholar
• Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (pp. 38–58). CRC press.
   Google Scholar
• Coulibaly, P., Anctil, F., Aravena, R., & Bobée, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4), 885–896. https://doi.org/10.1029/2000WR900368
   Web of Science ®Google Scholar
• Coulibaly, P., & Baldwin, C. K. (2005). Nonstationary hydrological time series forecasting using nonlinear dynamic methods. Journal of Hydrology, 307(1-4), 164–174. https://doi.org/10.1016/j.jhydrol.2004.10.008
   Web of Science ®Google Scholar
• Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1), 47–66. https://doi.org/10.1080/02626669809492102
   Web of Science ®Google Scholar
• De Silva, M., Weerakoon, S., & Herath, S. (2014). Modeling of event and continuous flow hydrographs with HEC–HMS: Case study in the kelani river basin, Sri Lanka. Journal of Hydrologic Engineering, 19(4), 800–806. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000846
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1002/joc.3754
   Web of Science ®Google Scholar
• Diaconescu, E. (2008). The use of NARX neural networks to predict chaotic time series. WSEAS Transactions on Computers Archive, 3, 182–191.
   Google Scholar
• Faruq, A., Abdullah, S. S., Marto, A., Abu Bakar, M. A., Mohd Hussein, S. F., & Che Razali, C. M. (2019). The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level. International Journal of Advances in Intelligent Informatics, 5(1), 1. https://doi.org/10.26555/ijain.v5i1.280
   Google Scholar
• Feldman, A. D. (2000). Hydrologic modeling system (HEC-HMS): Technical Refrence Manual.
   Google Scholar
• Ghose, D., Das, U., & Roy, P. (2018). Modeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network. Groundwater for Sustainable Development, 6, 263–269. https://doi.org/10.1016/j.gsd.2018.01.007
   Google Scholar
• Green, I. R. A., & Stephenson, D. (1986). Criteria for comparison of single event models. Hydrological Sciences Journal, 31(3), 395–411. https://doi.org/10.1080/02626668609491056
   Web of Science ®Google Scholar
• Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
   Web of Science ®Google Scholar
• Guzman, S. M., Paz, J. O., & Tagert, M. L. M. (2017). The Use of NARX neural networks to forecast daily Groundwater levels. Water Resources Management, 31(5), 1591–1603. https://doi.org/10.1007/s11269-017-1598-5
   Web of Science ®Google Scholar
• Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993. https://doi.org/10.1109/72.329697
   PubMed Web of Science ®Google Scholar
• Hasda, R., Rahaman, M. F., Jahan, C. S., Molla, K. I., & Mazumder, Q. H. (2020). Climatic data analysis for groundwater level simulation in drought prone barind tract, Bangladesh: Modelling approach using artificial neural network. Groundwater for Sustainable Development, 10, 100361. https://doi.org/10.1016/j.gsd.2020.100361
   Google Scholar
• Haykin, S. (2010). Neural Networks and Learning Machines, McMaster University, Ontario Canada, Pearson Education India.
   Google Scholar
• He, J., Valeo, C., Chu, A., & Neumann, N. F. (2011). Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection. Journal of Hydrology, 400(1-2), 10–23. https://doi.org/10.1016/j.jhydrol.2011.01.024
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1029/95WR01955
   Web of Science ®Google Scholar
• Izady, A., Davary, K., Alizadeh, A., Moghaddam Nia, A., Ziaei, A. N., & Hasheminia, S. M. (2013). Application of NN-ARX model to predict Groundwater levels in the neishaboor plain, Iran. Water Resources Management, 27(14), 4773–4794. https://doi.org/10.1007/s11269-013-0432-y
   Web of Science ®Google Scholar
• Klemeš, V. (1986). Operational testing of hydrological simulation models. Hydrological Sciences Journal, 31(1), 13–24. https://doi.org/10.1080/02626668609491024
   Web of Science ®Google Scholar
• Lee, C. C., & Sheridan, S. C. (2018). A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input. Environmental Research, 164, 53–64. https://doi.org/10.1016/j.envres.2018.02.020
   PubMed Web of Science ®Google Scholar
• Lee, C. C., Sheridan, S. C., Barnes, B. B., Hu, C., Pirhalla, D. E., Ransibrahmanakul, V., & Shein, K. (2017). The development of a non-linear autoregressive model with exogenous input (NARX) to model climate-water clarity relationships: Reconstructing a historical water clarity index for the coastal waters of the southeastern USA. Theoretical and Applied Climatology, 130(1-2), 557–569. https://doi.org/10.1007/s00704-016-1906-7
   Web of Science ®Google Scholar
• Legates, D. R., & Mccabe, G. J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. https://doi.org/10.1029/1998WR900018
   Web of Science ®Google Scholar
• Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environmental Modelling & Software, 15(1), 101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
   Web of Science ®Google Scholar
• Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), 891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
   Web of Science ®Google Scholar
• N. Moriasi, D., G. Arnold, J., W. Van Liew, M., L. Bingner, R., D. Harmel, R., & L. Veith, T. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900. doi:https://doi.org/10.13031/2013.23153
   Web of Science ®Google Scholar
• Musy, A., Hingray, B., & Picouet, C. (2014). Hydrology: A Science for engineers. CRC press.
   Google Scholar
• Nanda, T., Sahoo, B., Beria, H., & Chatterjee, C. (2016). A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products. Journal of Hydrology, 539, 57–73. https://doi.org/10.1016/j.jhydrol.2016.05.014
   Web of Science ®Google Scholar
• Nandalal, H., & Ratmayake, U. (2010). Event based modeling of a watershed using HEC-HMS. Engineer. Journal of the Institution of Engineers, Sri Lanka, 43.
   Google Scholar
• Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
   Google Scholar
• Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308–313. https://doi.org/10.1093/comjnl/7.4.308
   Web of Science ®Google Scholar
• Paik, K., Kim, J. H., Kim, H. S., & Lee, D. R. (2005). A conceptual rainfall-runoff model considering seasonal variation. Hydrological Processes, 19(19), 3837–3850. https://doi.org/10.1002/hyp.5984
   Web of Science ®Google Scholar
• Rahman, K. U., Balkhair, K. S., Almazroui, M., & Masood, A. (2017). Sub-catchments flow losses computation using muskingum–cunge routing method and HEC-HMS GIS based techniques, case study of Wadi Al-lith, Saudi arabia. Modeling Earth Systems and Environment, 3(1), 4. https://doi.org/10.1007/s40808-017-0268-1
   Web of Science ®Google Scholar
• Refsgaard, J. C. (1997). Parameterisation, calibration and validation of distributed hydrological models. Journal of Hydrology, 198(1-4), 69–97. https://doi.org/10.1016/S0022-1694(96)03329-X
   Web of Science ®Google Scholar
• Remesan, R., Shamim, M. A., Han, D., & Mathew, J. (2008, 12-15 Oct. 2008). ANFIS and NNARX based rainfall-runoff modeling. Paper presented at the 2008 IEEE International Conference on Systems, Man and Cybernetics.
   Google Scholar
• Rezaeianzadeh, M., Stein, A., Tabari, H., Abghari, H., Jalalkamali, N., Hosseinipour, E. Z., & Singh, V. P. (2013). Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. International Journal of Environmental Science and Technology, 10(6), 1181–1192. https://doi.org/10.1007/s13762-013-0209-0
   Web of Science ®Google Scholar
• Richards, J. A., & Jia, X. (1999). Remote sensing digital image analysis: An introduction. Springer Berlin Heidelberg.
   Google Scholar
• Roadknight, C. M., Balls, G. R., Mills, G. E., & Palmer-Brown, D. (1997). Modeling complex environmental data. IEEE Transactions on Neural Networks, 8(4), 852–862. https://doi.org/10.1109/72.595883
   PubMedGoogle Scholar
• Sahoo, A., Samantaray, S., & Ghose, D. K. (2019). Stream flow forecasting in mahanadi river basin using Artificial neural networks. Procedia Computer Science, 157, 168–174. https://doi.org/10.1016/j.procs.2019.08.154
   Google Scholar
• Samarasinghe, S. (2007). Neural networks for Applied Sciences and engineering. Auerbach Publications.
   Google Scholar
• Scharffenberg, B., Bartles, M., Brauer, T., Fleming, M., & Karlovits, G. (2018). Hydrologic modeling system (HEC-HMS) User's Manual: version 4.3. 0.
   Google Scholar
• Shamseldin, A. Y. (2010). Artificial neural network model for river flow forecasting in a developing country. Journal of Hydroinformatics, 12(1), 22–35. https://doi.org/10.2166/hydro.2010.027
   Web of Science ®Google Scholar
• Shamseldin, A. Y., & O’Connor, K. M. (2001). A non-linear neural network technique for updating of river flow forecasts. Hydrology and Earth System Sciences, 5(4), 577–598. https://doi.org/10.5194/hess-5-577-2001
   Web of Science ®Google Scholar
• Shen, H. Y., & Chang, L. C. (2013). Online multistep-ahead inundation depth forecasts by recurrent NARX networks. Hydrology and Earth System Sciences, 17(3), 935–945. https://doi.org/10.5194/hess-17-935-2013
   Web of Science ®Google Scholar
• Singh, N., & Chakrapani, G. J. (2015). ANN modelling of sediment concentration in the dynamic glacial environment of gangotri in himalaya. Environmental Monitoring and Assessment, 187(8), 494. https://doi.org/10.1007/s10661-015-4672-6
   PubMed Web of Science ®Google Scholar
• Srivastava, A. K., Srivastava, V. K., & Ullah, A. (1995). The coefficient of determination and its adjusted version in linear regression models. Econometric Reviews, 14(2), 229–240. https://doi.org/10.1080/07474939508800317
   Google Scholar
• Suykens, J. A. K., Vandewalle, J. P. L., & De Moor, B. L. R. (1996). Nonlinear system identification using neural networks. In Artificial neural networks for modelling and control of Non-linear systems. (pp. 37–82). Boston, MA: Springer US.
   Google Scholar
• Talei, A., Chua, L. H. C., & Wong, T. S. W. (2010). Evaluation of rainfall and discharge inputs used by adaptive network-based fuzzy inference systems (ANFIS) in rainfall–runoff modeling. Journal of Hydrology, 391(3-4), 248–262. https://doi.org/10.1016/j.jhydrol.2010.07.023
   Web of Science ®Google Scholar
• Tokar, A. S., & Markus, M. (2000). Precipitation-Runoff modeling using Artificial neural networks and conceptual models. Journal of Hydrologic Engineering, 5(2), 156–161.
   Web of Science ®Google Scholar
• Valizadeh, N., Mirzaei, M., Allawi, M. F., Afan, H. A., Mohd, N. S., Hussain, A., & El-Shafie, A. (2017). Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: State of the art. Natural Hazards, 86(3), 1377–1392. https://doi.org/10.1007/s11069-017-2740-7
   Web of Science ®Google Scholar
• Wang, Q., Huang, J., Liu, R., Men, C., Guo, L., Miao, Y., Jiao, L., Wang, Y., Shoaib, M., & Xia, X. (2020). Sequence-based statistical downscaling and its application to hydrologic simulations based on machine learning and big data. Journal of Hydrology, 124875.
   Google Scholar
• Wunsch, A., Liesch, T., & Broda, S. (2018). Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). Journal of Hydrology, 567, 743–758. https://doi.org/10.1016/j.jhydrol.2018.01.045
   Web of Science ®Google Scholar
• Yang, S., Yang, D., Chen, J., & Zhao, B. (2019). Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. Journal of Hydrology, 579, 124229. https://doi.org/10.1016/j.jhydrol.2019.124229
   Web of Science ®Google Scholar
• Yang, Y., Du, J., Cheng, L., & Xu, W. (2017). Applicability of TRMM satellite precipitation in driving hydrological model for identifying flood events: A case study in the xiangjiang river basin, China. Natural Hazards, 87(3), 1489–1505. https://doi.org/10.1007/s11069-017-2836-0
   Web of Science ®Google Scholar