Long-Term Groundwater-Level Forecasting in Shallow and Deep Wells Using Wavelet Neural Networks Trained by an Improved Harmony Search Algorithm

AbstractThis study proposes a model using wavelet neural networks (WNNs) trained by a novel improved harmony search (IHS) algorithm to forecast daily groundwater level (GWL) in a shallow well and a...

[1]  Özgür Kisi,et al.  Predicting groundwater level fluctuations with meteorological effect implications - A comparative study among soft computing techniques , 2013, Comput. Geosci..

[2]  Edgar N. Sánchez,et al.  Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[3]  Kwok-wing Chau,et al.  A novel hybrid neural network based on continuity equation and fuzzy pattern-recognition for downstream daily river discharge forecasting , 2015 .

[4]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[5]  Omid Bozorg Haddad,et al.  Levee Layouts and Design Optimization in Protection of Flood Areas , 2015 .

[6]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[7]  Taher Rajaee,et al.  Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model , 2015, Environmental Monitoring and Assessment.

[8]  Rajandrea Sethi,et al.  Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon , 2012, Eng. Appl. Artif. Intell..

[9]  Ferenc Szidarovszky,et al.  A neural network model for predicting aquifer water level elevations , 2005, Ground water.

[10]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[11]  K. P. Sudheer,et al.  Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India , 2010 .

[12]  Vahid Nourani,et al.  An ANN‐based model for spatiotemporal groundwater level forecasting , 2008 .

[13]  Omid Bozorg Haddad,et al.  Optimization Model for Design-Operation of Pumped-Storage and Hydropower Systems , 2014 .

[14]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[15]  Jun Guo,et al.  Monthly streamflow forecasting based on improved support vector machine model , 2011, Expert Syst. Appl..

[16]  Turgay PartalT. Partal.  River flow forecasting using different artificial neural network algorithms and wavelet transform , 2009 .

[17]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[18]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[19]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .

[20]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[21]  Taher Niknam,et al.  Probabilistic wind power forecasting using a novel hybrid intelligent method , 2016, Neural Computing and Applications.

[22]  S. S. Panda,et al.  Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks , 2010 .

[23]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[24]  Guy P. Nason,et al.  Wavelet Methods in Statistics with R , 2008 .

[25]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[26]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[27]  Ozgur Kisi,et al.  Wavelet-linear genetic programming: A new approach for modeling monthly streamflow , 2017 .

[28]  Hamidreza Zareipour,et al.  Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm , 2015 .

[29]  K. Chau,et al.  A hybrid model coupled with singular spectrum analysis for daily rainfall prediction , 2010 .

[30]  Halim Ceylan,et al.  Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .

[31]  Taher Rajaee,et al.  A wavelet-linear genetic programming model for sodium (Na + ) concentration forecasting in rivers , 2016 .

[32]  Alicia Troncoso Lora,et al.  Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours , 2002, DEXA.

[33]  Omid Bozorg Haddad,et al.  Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming , 2015 .

[34]  Omid Bozorg Haddad,et al.  Determination of Irrigation Allocation Policy under Climate Change by Genetic Programming , 2015 .

[35]  T. Breurch,et al.  A simple test for heteroscedasticity and random coefficient variation (econometrica vol 47 , 1979 .

[36]  Z. Hou,et al.  A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China , 2015, Arabian Journal of Geosciences.

[37]  B. K. Panigrahi,et al.  A hybrid wavelet-ELM based short term price forecasting for electricity markets , 2014 .

[38]  Ping Li,et al.  Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. , 2009 .

[39]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

[40]  Jiazheng Lu,et al.  Multi-objective optimization of empirical hydrological model for streamflow prediction , 2014 .

[41]  Vahid Nourani,et al.  Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling , 2015 .

[42]  Honey Badrzadeh,et al.  Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting , 2013 .

[43]  Taher Rajaee,et al.  Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine , 2017 .

[44]  Eloy Kaviski,et al.  Monthly rainfall–runoff modelling using artificial neural networks , 2011 .

[45]  H. Akbari,et al.  Climate Change Impact on Probable Maximum Precipitation in Chenar-Rahdar River Basin , 2015 .

[46]  Qiao Yan,et al.  Application of integrated ARIMA and RBF network for groundwater level forecasting , 2016, Environmental Earth Sciences.

[47]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[48]  Mustafa Ergil,et al.  Prediction of dissolved oxygen in River Calder by noise elimination time series using wavelet transform , 2016, J. Exp. Theor. Artif. Intell..

[49]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[50]  Vahid Nourani,et al.  Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study , 2011 .

[51]  H. Akbari,et al.  Climate Change Impact on Intensity-Duration-Frequency Curves in Chenar-Rahdar River Basin , 2015 .

[52]  Roger Koenker,et al.  A note on studentizing a test for heteroscedasticity , 1981 .

[53]  Paulin Coulibaly,et al.  Bayesian neural network for rainfall‐runoff modeling , 2006 .

[54]  David S. Stoffer,et al.  Time series analysis and its applications , 2000 .

[55]  Taher Niknam,et al.  Speed control of electrical vehicles: a time-varying proportional–integral controller-based type-2 fuzzy logic , 2016 .

[56]  Mahmoud Zemzami,et al.  Improvement of artificial neural networks to predict daily streamflow in a semi-arid area , 2016 .

[57]  A. W. Jayawardena,et al.  Runoff Forecasting Using RBF Networks with OLS Algorithm , 1998 .

[58]  D. Kavetski,et al.  Confronting Input Uncertainty in Environmental Modelling , 2013 .

[59]  Adam P. Piotrowski,et al.  Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction? , 2016, Water Resources Management.

[60]  X. Y. Chen,et al.  A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model , 2015, Eng. Appl. Artif. Intell..

[61]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[62]  Yang Hong,et al.  Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method , 2012 .

[63]  Kwok-wing Chau,et al.  Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines , 2015 .

[64]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[65]  Gwo-Fong Lin,et al.  An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model , 2011 .

[66]  H. White.  A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[67]  Özgür Kisi,et al.  Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations , 2011, Comput. Geosci..

[68]  Yi Liu,et al.  A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization , 2013, Water Resources Management.

[69]  J. Adamowski.  Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis , 2008 .

[70]  Martin F. Lambert,et al.  Bayesian training of artificial neural networks used for water resources modeling , 2005 .

[71]  S. Sorooshian,et al.  Stochastic parameter estimation procedures for hydrologie rainfall‐runoff models: Correlated and heteroscedastic error cases , 1980 .

[72]  Ozgur Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations , 2012 .

[73]  C. L. Wu,et al.  Methods to improve neural network performance in daily flows prediction , 2009 .

[74]  W. Li,et al.  Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series , 2006 .