Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

Adamowski, 2011, A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28, 10.1016/j.jhydrol.2011.06.013 Adamowski, 2010, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, J. Hydrol., 390, 85, 10.1016/j.jhydrol.2010.06.033 Adamowski, 2009, Development of a new method of wavelet aided trend detection and estimation, Hydrol. Process., 23, 2686, 10.1002/hyp.7260 Aghbashlo, 2016, The use of ELM–WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste, Energy, 94, 443, 10.1016/j.energy.2015.11.008 Amanifard, 2008, Modeling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks, Energy Convers. Manag., 49, 2588, 10.1016/j.enconman.2008.05.025 Austin, 2004, Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality, J. Clin. Epidemiol., 57, 1138, 10.1016/j.jclinepi.2004.04.003 Barzegar, 2016, Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction, Model. Earth Syst. Environ., 2, 26, 10.1007/s40808-015-0072-8 Barzegar, 2016, Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran, Stoch. Env. Res. Risk A., 30, 1797, 10.1007/s00477-016-1213-y Barzegar, 2017, Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model, Stoch. Env. Res. Risk A. Barzegar, 2017, Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain, northwest of Iran, Environ. Earth Sci., 76, 297, 10.1007/s12665-017-6612-y Belayneh, 2016, Short-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet transforms and machine learning methods, Sustain. Water Resour. Manag., 2, 87, 10.1007/s40899-015-0040-5 Belayneh, 2016, Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction, Atmos. Res., 172–173, 37, 10.1016/j.atmosres.2015.12.017 Borsi, 2013, Modeling unsaturated zone flow and runoff processes by integrating MODFLOW-LGR and VSF, and creating the new CFL package, J. Hydrol., 488, 33, 10.1016/j.jhydrol.2013.02.020 Chang, 2006, Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Adv. Water Resour., 29, 1, 10.1016/j.advwatres.2005.04.015 Cherkassky, 2009, Another look at statistical learning theory and regularization, Neural Netw., 22, 958, 10.1016/j.neunet.2009.04.005 Daliakopoulose, 2005, Groundwater level forecasting using artificial neural networks, J. Hydrol., 309, 229, 10.1016/j.jhydrol.2004.12.001 DeGiorgi, 2016, Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine, Energy, 107, 360, 10.1016/j.energy.2016.04.020 Deo, 2016, Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model, Stoch. Env. Res. Risk A. Deo, 2017, Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle, Environ. Res., 155, 141, 10.1016/j.envres.2017.01.035 Dghasi, 2013, A comparative study between discrete wavelet transform and maximal overlap discrete wavelet transform for testing stationarity, Int. J. Math. Comput. Phys. Electr. Comput. Eng., 7, 1677 Ding, 2015, A wavelet extreme learning machine, Neural Comput. & Applic., 27, 1033, 10.1007/s00521-015-1918-8 Dorna, 2012, A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides, Expert Syst. Appl., 39, 12268, 10.1016/j.eswa.2012.04.046 Farlow, 1984, Self–organizing method in modeling Fijani, 2013, Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran, J. Hydrol., 503, 89, 10.1016/j.jhydrol.2013.08.038 Fijani, 2017, Analysis and assessment of hydrochemical characteristics of Maragheh-Bonab plain aquifer, northwest of Iran, Water Resour. Manag., 10.1007/s11269-016-1390-y French, 1992, Rainfall forecasting in space and time using neural network, J. Hydrol., 137, 1, 10.1016/0022-1694(92)90046-X Ghouti, 2013, Mobility prediction in mobile ad hoc networks using extreme learning machines, Procedia Comput. Sci., 19, 305, 10.1016/j.procs.2013.06.043 Gong, 2016, A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida, Water Resour. Manag., 30, 375, 10.1007/s11269-015-1167-8 vanHeijst, 2008, A support system for predicting eBay end prices, Decis. Support. Syst., 44, 970, 10.1016/j.dss.2007.11.004 Huang, 2004, Extreme learning machine: a new learning scheme of feedforward neural networks, 2, 985 Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126 Huot, 2007, Does chlorophyll a provide the best index of phytoplankton biomass for primary productivity studies?, Biogeosci. Discuss., 4, 707, 10.5194/bgd-4-707-2007 Ivakhnenko, 1971, Polynomial theory of complex systems, IEEE Trans. Syst. Man. Cybern., 1, 364, 10.1109/TSMC.1971.4308320 Ivakhnenko, 2000, Problems of further development of the group method of data handling algorithms. Part 1, Pattern Recognit. Image Anal., 110, 187 Jafari, 2011, Committee neural networks with fuzzy genetic algorithm, J. Pet. Sci. Eng., 76, 217, 10.1016/j.petrol.2011.01.006 Jung, 2015, Statistical modeling of near-surface wind speed: a case study from Baden-Wuerttemberg (Southwest Germany), Austin. J. Earth. Sci., 2, 1006 Kallache, 2005, Trend assessment: applications for hydrology and climate research, Nonlinear Process. Geophys., 12, 201, 10.5194/npg-12-201-2005 Kim, 2008, Development and application of the integrated SWAT–MODFLOW model, J. Hydrol., 356, 1, 10.1016/j.jhydrol.2008.02.024 Kinzelbach, 2003, Sustainable groundwater management - problems and scientific tools, Episodes J. Int. Geosci., 26, 279, 10.18814/epiiugs/2003/v26i4/002 Kisi, 2011, Wavelet regression model as an alternative to neural networks for river stage forecasting, Water Resour. Manag., 25, 579, 10.1007/s11269-010-9715-8 Labat, 2005, Recent advances in wavelet analyses: part 1. A review of concepts, J. Hydrol., 314, 275, 10.1016/j.jhydrol.2005.04.003 Lambert, 2016, Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling, Math. Comput. Simul., 128, 42, 10.1016/j.matcom.2016.04.005 Lian, 2014, Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level, Stoch. Env. Res. Risk A., 28, 1957, 10.1007/s00477-014-0875-6 Lima, 2015, Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation, Environ. Model. Softw., 73, 175, 10.1016/j.envsoft.2015.08.002 Liu, 2015, Is extreme learning machine feasible? A theoretical assessment (Part I), IEEE Trans. Neural Netw. Learn. Syst., 7, 10.1109/TNNLS.2014.2335212 Mehra, 1977, Group method of data handling (GMDH): review and experience, IEEE Conf. Decis. Control, 16, 29 Mohanty, 2010, Artificial neural network modeling for groundwater level forecasting in a River Island of eastern India, Water Resour. Manag., 24, 1845, 10.1007/s11269-009-9527-x Moosavi, 2017, Development of a hybrid wavelet packet–group method of data handling (WPGMDH) model for runoff forecasting, Water Resour. Manag., 31, 43, 10.1007/s11269-016-1507-3 Najafzadeh, 2013, Neuro–fuzzy GMDH systems to predict the scour pile groups due to waves, J. Comput. Civ. Eng. Najafzadeh, 2015, Application of improved neuro-fuzzy GMDH to predict scour downstream of sluice gates, Earth Sci. Inf., 8, 187, 10.1007/s12145-014-0144-8 Najafzadeh, 2012, Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling, Neural Comput. & Applic., 24, 629 Najafzadeh, 2013, Abutment scour in live-bed and clear-water using GMDH network, Water Sci. Technol., 67, 1121, 10.2166/wst.2013.670 Nalley, 2012, Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008), J. Hydrol., 475, 204, 10.1016/j.jhydrol.2012.09.049 Nian, 2014, Extreme learning machine towards dynamic model hypothesis in fish ethology research, Neurocomputing, 128, 273, 10.1016/j.neucom.2013.03.054 Nievergelt, 2001, 297 Percival, 2000, 594 Raghavendra, 2016, Multistep ahead groundwater level time-series forecasting using Gaussian process regression and ANFIS, 289 Rathinasamy, 2011, Wavelet–Volterra coupled model for monthly stream flow forecasting, J. Hydrol., 450–451, 320 Rathinasamy, 2012, Comparative study of different wavelets for hydrologic forecasting, Comput. Geosci., 46, 284, 10.1016/j.cageo.2011.12.015 Rathinasamy, 2015, Multiscale stream flow forecasting using a new Bayesian model average based ensemble multi-wavelet Volterra nonlinear method, J. Hydrol., 507, 186, 10.1016/j.jhydrol.2013.09.025 Sajjadi, 2016, Extreme learning machine for prediction of heat load in district heating systems, Energ. Buildings, 122, 222, 10.1016/j.enbuild.2016.04.021 Sang, 2010, Wavelet cross-correlation method for hydrologic time series analysis, J. Hydrol. Eng., 11, 1172 Shamshirband, 2016, Application of extreme learning machine for estimation of wind speed distribution, Clim. Dyn., 46, 1893, 10.1007/s00382-015-2682-2 Shiri, 2013, Predicting groundwater level fluctuations with meteorological effect implications- a comparative study among soft computing techniques, Comput. Geosci., 56, 32, 10.1016/j.cageo.2013.01.007 Silhavy, 2017, Analysis and selection of a regression model for the use case points method using a stepwise approach, J. Syst. Softw., 125, 1, 10.1016/j.jss.2016.11.029 Sudheer, 2012, Particle swarm optimization trained neural network for aquifer parameter estimation, KSCE J. Civ. Eng., 16, 298, 10.1007/s12205-012-1452-5 Sudheer, 2002, A data-driven algorithm for constructing artificial neural network rainfall-runoff models, Hydrol. Process., 16, 1325, 10.1002/hyp.554 Sun, 2016, Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest, Hydrol. Earth Syst. Sci., 20, 1405, 10.5194/hess-20-1405-2016 Suryanarayana, 2014, An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India, Neurocomputing, 145, 324, 10.1016/j.neucom.2014.05.026 Thiessen, 1911, Precipitation averages for large areas, Mon. Weather Rev., 39, 1082 Tiwari, 2013, Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap neural network models, Water Resour., 49, 6486, 10.1002/wrcr.20517 Tiwari, 2010, Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach, J. Hydrol., 394, 458, 10.1016/j.jhydrol.2010.10.001 Tsai, 2016, GMDH algorithms applied to turbidity forecasting, Appl. Water Sci. Volterra, 1959 Wang, 2014, Online sequential extreme learning machine with kernels for nonstationary time series prediction, Neurocomputing, 145, 90, 10.1016/j.neucom.2014.05.068 Wang, 2014, Fast prediction of protein–protein interaction sites based on extreme learning machines, Neurocomputing, 128, 258, 10.1016/j.neucom.2012.12.062 Wong, 2015, Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search, Renew. Energy, 74, 640, 10.1016/j.renene.2014.08.075 Yoon, 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128, 10.1016/j.jhydrol.2010.11.002 Yu, 2014, Bankruptcy prediction using extreme learning machine and financial expertise, Neurocomputing, 128, 296, 10.1016/j.neucom.2013.01.063 Zhu, 2014, MODWT–ARMA model for time series prediction, Appl. Math. Model., 38, 1859, 10.1016/j.apm.2013.10.002