Investigation and Comparison of Performance of Modern Intelligent tTechniques in Groundwater Nitrate Simulation
Subject Areas : Water Resources
Jafar Seraji
1
(
Department of Water Engineering, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran
)
Keywords: Artificial Neural Network, simulation, Nitrate, Supported Vector Machines, Neopoietic,
Abstract :
Today, due to recent drought, one of the main sources of drinking water in the country is underground resources, and also nitrate is one of the most important pollutants of groundwater resources, which has adverse effects on people's health. The present study seeks to compare and provide an efficient and innovative technique for simulating and predicting nitrate in these resources. Therefore, three artificial neural networks (ANN) models of neuro-fuzzy inference system (ANFIS) and vector-supported vector (SVM) are compared in simulation as a data-driven tool. Simulation based on observation samples from wells in the aquifer under study for 13 years and the modeling period has been selected monthly. Estimates of model simulations include magnesium (Mg), bicarbonate (Hco3), calcium (Ca), sodium (Na). First, the heterogeneous simulation of heterogeneity is carried out on different makeup. Based on the results of the evaluation of the neo-Frazi system The correlation coefficient of R2 = 9978/0 and MS2 = 0002 have better capability and capability.
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