Community vulnerability to hazards: introducing local expert knowledge into the equation |
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| Authors: | Yago Martín Marcos Rodrigues Mimbrero María Zúñiga-Antón |
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| Institution: | 1.Center for Interdisciplinary Research,International Balck Sea University,Tbilisi,Georgia;2.Young Researchers and Elite Club, Hamedan Branch,Islamic Azad University,Hamedan,Iran;3.Department of Water Engineering,Shahid Bahonar University of Kerman,Kerman,Iran |
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| 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. |
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