Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

References

• Abdullah, S. S., Malek, M. a., Abdullah, N. S., Kisi, O., & Yap, K. S. (2015). Extreme learning machines: A new approach for prediction of reference evapotranspiration. Journal of Hydrology, 527, 184–195. doi: 10.1016/j.jhydrol.2015.04.073
   Web of Science ®Google Scholar
• Adamowski, J., Chan, H. F., Prasher, S. O., & Sharda, V. N. (2012). Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. Journal of Hydroinformatics, 14(3), 731–744. doi: 10.2166/hydro.2011.044
   Web of Science ®Google Scholar
• Adnan, R. M., Malik, A., Kumar, A., Parmar, K. S., & Kisi, O. (2019). Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arabian Journal of Geosciences, 12(20), 606. doi: 10.1007/s12517-019-4781-6
   Web of Science ®Google Scholar
• Burt, C. M., Mutziger, A. J., Allen, R. G., & Howell, T. A. (2005). Evaporation research: Review and interpretation. Journal of Irrigation and Drainage Engineering, 131(1), 37–58. doi: 10.1061/(ASCE)0733-9437(2005)131:1(37)
   Web of Science ®Google Scholar
• Danandeh Mehr, A., & Kahya, E. (2017). A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction. Journal of Hydrology, 549, 603–615. doi: 10.1016/j.jhydrol.2017.04.045
   Web of Science ®Google Scholar
• Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505, 240–249. doi: 10.1016/j.jhydrol.2013.10.003
   Web of Science ®Google Scholar
• Danandeh Mehr, A., Kahya, E., & Yerdelen, C. (2014). Linear genetic programming application for successive-station monthly streamflow prediction. Computers and Geosciences, 70, 63–72. doi: 10.1016/j.cageo.2014.04.015
   Web of Science ®Google Scholar
• Danandeh Mehr, A., & Nourani, V. (2017). A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling. Environmental Modelling and Software, 92, 239–251. doi: 10.1016/j.envsoft.2017.03.004
   Web of Science ®Google Scholar
• De Andrés, J., Lorca, P., de Cos Juez, F. J., & Sánchez-Lasheras, F. (2011). Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and multivariate adaptive regression Splines (MARS). Expert Systems with Applications, 38(3), 1866–1875. doi: 10.1016/j.eswa.2010.07.117
   Web of Science ®Google Scholar
• Deo, R. C., Kisi, O., & Singh, V. P. (2017). Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmospheric Research, 184, 149–175. doi: 10.1016/j.atmosres.2016.10.004
   Web of Science ®Google Scholar
• Deo, R. C., Samui, P., & Kim, D. (2016). Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environmental Research and Risk Assessment, 30(6), 1769–1784. doi: 10.1007/s00477-015-1153-y
   Web of Science ®Google Scholar
• Doorenbos, J., & Pruitt, W. O. (1977). Guidelines for predicting crop water requirements (Irrigation and Drainage Paper No. 24, FAO). doi: 10.2514/6.2014-2117
   Google Scholar
• Friedman, J. H. (1991). Multivariate adaptive regression Splines. The Annals of Statistics, 19, 1–67. doi: 10.1214/aos/1176347963
   Web of Science ®Google Scholar
• Ghorbani, M. A., Deo, R. C., Yaseen, Z. M., Kashani, M. H., & Mohammadi, B. (2017). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: Case study in North Iran. Theoretical and Applied Climatology, 133, 1119–1131. doi: 10.1007/s00704-017-2244-0
   Web of Science ®Google Scholar
• Ghorbani, M. A., Khatibi, R., Karimi, V., Yaseen, Z. M., & Zounemat-Kermani, M. (2018). Learning from multiple models using artificial intelligence to Improve model prediction accuracies: Application to river flows. Water Resources Management, 32, 4201–4215. doi: 10.1007/s11269-018-2038-x
   Web of Science ®Google Scholar
• Goyal, M. K. (2014). Modeling of sediment yield prediction using M5 model tree algorithm and wavelet regression. Water Resources Management 28, 1991–2003. doi: 10.1007/s11269-014-0590-6
   Web of Science ®Google Scholar
• Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., & Pandey, A. (2014). Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11), 5267–5276. doi: 10.1016/j.eswa.2014.02.047
   Web of Science ®Google Scholar
• Griffiths, J. F. (1966). Another evaporation formula. Agricultural Meteorology, 3(3–4), 257–261. doi: 10.1016/0002-1571(66)90033-1
   Google Scholar
• Guven, A. (2009). Linear genetic programming for time-series modelling of daily flow rate. Journal of Earth System Science, 118(2), 137–146. doi: 10.1007/s12040-009-0022-9
   Web of Science ®Google Scholar
• Guven, A., & Kisi, O. (2013). Monthly pan evaporation modeling using linear genetic programming. Journal of Hydrology, 503, 178–185. doi: 10.1016/j.jhydrol.2013.08.043
   Web of Science ®Google Scholar
• Kashani, M. H., Ghorbani, M. A., Shahabi, M., Naganna, S. R., & Diop, L. (2020). Multiple AI model integration strategy—application to saturated hydraulic conductivity prediction from easily available soil properties. Soil and Tillage Research, 196, 104449. doi: 10.1016/j.still.2019.104449
   Web of Science ®Google Scholar
• Keshtegar, B., Piri, J., & Kisi, O. (2016). A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Computers and Electronics in Agriculture, 127, 120–130. doi: 10.1016/j.compag.2016.05.018
   Web of Science ®Google Scholar
• Khatibi, R., Ghorbani, M. A., Jani, R., & Servati, M. (2018). Soil cation exchange capacity predicted by learning from multiple modelling: Forming multiple models run by SVM to Learn from ANN and its hybrid with firefly algorithm. In D. Kim, R. Deo, & P. Samui (Eds.), Handbook of research on predictive modeling and optimization methods in science and engineering (pp. 465–480). Hershey, PA: IGI Global.
   Google Scholar
• Khatibi, R., Ghorbani, M. A., & Pourhosseini, F. A. (2017). Stream flow predictions using nature-inspired firefly algorithms and a multiple model strategy–directions of innovation towards next generation practices. Advanced Engineering Informatics, 34, 80–89. doi: 10.1016/j.aei.2017.10.002
   Web of Science ®Google Scholar
• Kisi, O. (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528, 312–320. doi: 10.1016/j.jhydrol.2015.06.052
   Web of Science ®Google Scholar
• Kişi, O., & Cimen, M. (2009). Evapotranspiration modelling using support vector machines/Modélisation de l’évapotranspiration à l’aide de ‘support vector machines’. Hydrological Sciences Journal, 54(5), 918–928. doi: 10.1623/hysj.54.5.918
   Web of Science ®Google Scholar
• Kisi, O., Genc, O., Dinc, S., & Zounemat-Kermani, M. (2016). Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Computers and Electronics in Agriculture, 122, 112–117. doi: 10.1016/j.compag.2016.01.026
   Web of Science ®Google Scholar
• Kisi, O., & Heddam, S. (2019). Evaporation modelling by heuristic regression approaches using only temperature data. Hydrological Sciences Journal, 64(6), 653–672. doi: 10.1080/02626667.2019.1599487
   Web of Science ®Google Scholar
• Malik, A., & Kumar, A. (2015). Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple linear regression. Water Resources Management, 29(6), 1859–1872. doi: 10.1007/s11269-015-0915-0
   Web of Science ®Google Scholar
• Malik, A., & Kumar, A. (2018). Comparison of soft-computing and statistical techniques in simulating daily river flow: A case study in India. Journal of Soil and Water Conservation, 17(2), 192. doi: 10.5958/2455-7145.2018.00029.2
   Google Scholar
• Malik, A., Kumar, A., Ghorbani, M. A., Kashani, M. H., Kisi, O., & Kim, S. (2019). The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: Case study of Uttarakhand State. Hydrology Research, 50, 1623–1644. doi: 10.2166/nh.2019.059
   Web of Science ®Google Scholar
• Malik, A., Kumar, A., & Kisi, O. (2017). Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models. Computers and Electronics in Agriculture, 143, 302–313. doi: 10.1016/j.compag.2017.11.008
   Web of Science ®Google Scholar
• Malik, A., Kumar, A., & Kisi, O. (2018). Daily Pan evaporation estimation using heuristic methods with gamma test. Journal of Irrigation and Drainage Engineering, 144(9), 04018023. doi: 10.1061/(asce)ir.1943-4774.0001336
   Web of Science ®Google Scholar
• Malik, A., Kumar, A., & Singh, R. P. (2019). Application of heuristic approaches for prediction of hydrological drought using multi-scalar streamflow drought index. Water Resources Management, 33, 3985–4006. doi: 10.1007/s11269-019-02350-4
   Web of Science ®Google Scholar
• Moghaddamnia, a., Ghafari Gousheh, M., Piri, J., Amin, S., & Han, D. (2009). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), 88–97. doi: 10.1016/j.advwatres.2008.10.005
   Web of Science ®Google Scholar
• Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193(1032), 120–145. doi: 10.1098/rspa.1948.0037
   PubMed Web of Science ®Google Scholar
• Piri, J., Amin, S., Moghaddamnia, A., Keshavarz, A., Han, D., & Remesan, R. (2009). Daily pan evaporation modeling in a hot and dry climate. Journal of Hydrologic Engineering, 14(8), 803–811. doi: 10.1061/(ASCE)HE.1943-5584.0000056
   Web of Science ®Google Scholar
• Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of the surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81–92. doi: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
   Web of Science ®Google Scholar
• Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K.-W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187. doi: 10.1080/19942060.2018.1564702
   Web of Science ®Google Scholar
• Qutbudin, I., Shiru, M. S., Sharafati, A., Ahmed, K., Al-Ansari, N., Yaseen, Z. M., … Wang, X. (2019). Seasonal drought pattern changes Due to climate variability: Case study in Afghanistan. Water, 11(5), 1096. doi: 10.3390/w11051096
   Web of Science ®Google Scholar
• Rahimikhoob, A. (2014). Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment. Water Resources Management, 28(3), 657–669. doi: 10.1007/s11269-013-0506-x
   Web of Science ®Google Scholar
• Remesan, R., Shamim, M. A., & Han, D. (2008). Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes, 22(21), 4301–4309. doi: 10.1002/hyp.7044
   Web of Science ®Google Scholar
• Remesan, R., Shamim, M. A., Han, D., & Mathew, J. (2009). Runoff prediction using an integrated hybrid modelling scheme. Journal of Hydrology, 372(1–4), 48–60. doi: 10.1016/j.jhydrol.2009.03.034
   Web of Science ®Google Scholar
• Rezaie-Balf, M., Kisi, O., & Chua, L. H. C. (2019). Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation. Hydrology Research, 50(2), 498–516. doi: 10.2166/nh.2018.050
   Web of Science ®Google Scholar
• Salih, S. Q., sharafati, A., Khosravi, K., Faris, H., Kisi, O., Tao, H., … Yaseen, Z. M. (2020). River suspended sediment load prediction based on river discharge information: Application of newly developed data mining models. Hydrological Sciences Journal. doi: 10.1080/02626667.2019.1703186
   Web of Science ®Google Scholar
• Searson, D. P. (2015). GPTIPS 2: An open-source software platform for symbolic data mining. In Handbook of genetic programming applications (pp. 551–573). doi: 10.1007/978-3-319-20883-1_22
   Google Scholar
• Sebbar, A., Heddam, S., & Djemili, L. (2019). Predicting daily Pan evaporation (Epan) from Dam reservoirs in the mediterranean regions of Algeria: OPELM vs OSELM. Environmental Processes, 6, 309–319. doi: 10.1007/s40710-019-00353-2
   Google Scholar
• Sharafati, A., Khosravi, K., Khosravinia, P., Ahmed, K., Salman, S. A., Mundher, Z., & Shamsuddin, Y. (2019). The potential of novel data mining models for global solar radiation prediction. International Journal of Environmental Science and Technology, 0123456789. doi: 10.1007/s13762-019-02344-0
   Web of Science ®Google Scholar
• Sharafati, A., Tafarojnoruz, A., Shourian, M., & Yaseen, Z. M. (2019). Simulation of the depth scouring downstream sluice gate: The validation of newly developed data-intelligent models. Journal of Hydro-Environment Research. doi: 10.1016/j.jher.2019.11.002
   Web of Science ®Google Scholar
• Sharafati, A., Yasa, R., & Azamathulla, H. M. (2018). Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. Journal of Pipeline Systems Engineering and Practice, 9(4), 04018024. doi: 10.1061/(ASCE)PS.1949-1204.0000347
   Web of Science ®Google Scholar
• Shoaib, M., Shamseldin, A. Y., Melville, B. W., & Khan, M. M. (2015). Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. Journal of Hydrology, 527, 326–344. doi: 10.1016/j.jhydrol.2015.04.072
   Web of Science ®Google Scholar
• Singh, A., Malik, A., Kumar, A., & Kisi, O. (2018). Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arabian Journal of Geosciences, 11(11). doi: 10.1007/s12517-018-3614-3
   Web of Science ®Google Scholar
• Singh, K. K., Pal, M., & Singh, V. P. (2010). Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree. Water Resources Management, 24(10), 2007–2019. doi: 10.1007/s11269-009-9535-x
   Web of Science ®Google Scholar
• Tabari, H., Talaee, P. H., & Abghari, H. (2012). Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron. Meteorology and Atmospheric Physics, 116(3–4), 147–154. doi: 10.1007/s00703-012-0184-x
   Web of Science ®Google Scholar
• Tao, H., Diop, L., Bodian, A., Djaman, K., Ndiaye, P. M., & Yaseen, Z. M. (2018). Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso. Agricultural Water Management, 208, 140–151. doi: 10.1016/j.agwat.2018.06.018
   Web of Science ®Google Scholar
• Tikhamarine, Y., Malik, A., Kumar, A., Souag-Gamane, D., & Kisi, O. (2019). Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological Sciences Journal, 64(15), 1824–1842. doi: 10.1080/02626667.2019.1678750
   Web of Science ®Google Scholar
• Tsui, A. P. M., Jones, A. J., & De Oliveira, A. G. (2002). The construction of smooth models using irregular embeddings determined by a gamma test analysis. Neural Computing & Applications, 10(4), 318–329. doi: 10.1007/s005210200004
   Web of Science ®Google Scholar
• Wang, L., Kisi, O., Hu, B., Bilal, M., Zounemat-Kermani, M., & Li, H. (2017). Evaporation modelling using different machine learning techniques. International Journal of Climatology, 37(S1), 1076–1092. doi: 10.1002/joc.5064
   Web of Science ®Google Scholar
• Wang, L., Kisi, O., Zounemat-Kermani, M., & Li, H. (2017). Pan evaporation modeling using six different heuristic computing methods in different climates of China. Journal of Hydrology, 544, 407–427. doi: 10.1016/j.jhydrol.2016.11.059
   Web of Science ®Google Scholar
• Wang, L., Niu, Z., Kisi, O., Li, C., & Yu, D. (2017). Pan evaporation modeling using four different heuristic approaches. Computers and Electronics in Agriculture, 140, 203–213. doi: 10.1016/j.compag.2017.05.036
   Web of Science ®Google Scholar
• Yaseen, Z. M., Al-Juboori, A. M., Beyaztas, U., Al-Ansari, N., Chau, K.-W., Qi, C., … Shahid, S. (2019). Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 70–89. doi: 10.1080/19942060.2019.1680576
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Zorn, C. R., & Shamseldin, A. Y. (2015). Peak flood estimation using gene expression programming. Journal of Hydrology, 531, 1122–1128. doi: 10.1016/j.jhydrol.2015.11.018
   Web of Science ®Google Scholar