Simulation of Electrical Conductivity of Behbahan Plain Using ANN and ANN-PSO Models
One of the main aims of water resource planning and management is to estimate and predict groundwater quality parameters which would be used in decision-making. In this regard, many models have been developed which proposed better managements in order to maintain water quality. Most of these models require input parameters which are hardly available or their measurements are time consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by human's brain are a better choice. The present studied stimulated the electrical conductivity of water quality parameters of Behbahan Plain, using ANN and ANN+PSO models and in the end compared their results with measured data. Data for NO3-, EC, Ca2+, Mg2+, SO42-, HCO3-, CL-, K+, TH and pH were collected during 2009-2016 as input data. The results indicated that the highest prediction accuracy of quality parameters was related to the ANN + PSO model so that the MAE and RMSE statistics had the minimum and had the maximum value for the model. Considering the high efficiency of artificial neural network model, by training the Particle Swarm Optimization algorithm, it can be used in order to make managerial decisions and ensure the results of monitoring and reducing costs.
Journal of Water & Wastewater Science and Engineering, Volume:4 Issue:1, 2019
34 - 41
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