Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data

Ozgur Kisi (), Meysam Alizamir and Mohammad Zounemat-Kermani
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Ozgur Kisi: International Balck Sea University
Meysam Alizamir: Islamic Azad University
Mohammad Zounemat-Kermani: Shahid Bahonar University of Kerman

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 87, issue 1, No 18, 367-381

Abstract: Abstract The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The input combinations determined using auto-, partial auto- and cross-correlation analyses and tried for each model are: (i) GWL t−1 and GWL t−2; (ii) GWL t−1, GWL t−2 and P t ; (iii) GWL t−1, GWL t−2 and E t ; (iv) GWL t−1, GWL t−2, P t and E t ; (v) GWL t−1, GWL t−2 and P t−1 where GWL t , P t and E t indicate the GWL, precipitation and evaporation at time t, individually. The optimal ANN-GA, ANN-PSO and ANN-ICA models were obtained by trying various control parameters. The best accuracies of the ANN-GA, ANN-PSO and ANN-ICA models were obtained from input combination (i). The mean square error accuracies of the ANN-GA and ANN-ICA models were increased by 165 and 124% using ANN-PSO model. The results indicated that the ANN-PSO model performed better than the other models in modeling monthly groundwater levels.

Keywords: Groundwater fluctuations; Evolutionary neural networks; Genetic algorithm; Particle swarm optimization; Imperialist competitive algorithm; Modeling (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-017-2767-9

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