Environment
Conservation Journal

"An International Journal Devoted to Conservation of Environment"

(A PEER REVIEWED JOURNAL)

ISSN: 2278-5124 (Online) :: ISSN: 0972-3099 (Print)

Environment
Conservation Journal

"An International Journal Devoted to Conservation of Environment"

(A PEER REVIEWED JOURNAL)

ISSN: 2278-5124 (Online) :: ISSN: 0972-3099 (Print)

Environment
Conservation Journal

"An International Journal Devoted to Conservation of Environment"

(A PEER REVIEWED JOURNAL)

ISSN: 2278-5124 (Online) :: ISSN: 0972-3099 (Print)

Environment
Conservation Journal

"An International Journal Devoted to Conservation of Environment"

(A PEER REVIEWED JOURNAL)

ISSN: 2278-5124 (Online) :: ISSN: 0972-3099 (Print)

Environment
Conservation Journal

"An International Journal Devoted to Conservation of Environment"

(A PEER REVIEWED JOURNAL)

ISSN: 2278-5124 (Online) :: ISSN: 0972-3099 (Print)

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Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain

Tahereh Darini and Amir Jalalkamali

Department of water engineering, Kerman Branch, Islamic Azad University, Kerman

Abstract

Groundwater resources are one considered as one of the most common and important resources of drinking, agriculture and industry water. Due to the lowering of groundwater levels and its volatility, groundwater quality is of utmost importance. The aim of this study is to identify the predictive ability of artificial neural network of Multi-Layer Perceptron (MLP) and Support Vector Machine model and adaptive neuro-fuzzy inference system in which the quality of groundwater in Sirjan Plain has been predicted. A case study was conducted on the Sirjan Plain located in the city of Sirjan in Kerman province. For this purpose, the data of rainfall, the water level in wells and UTM coordinates of intended wells have been used as input combinations and qualitative parameters of the water of wells as output parameters. After initial processes such as normalization, for double-layer neural network, 85% of data were used for training and 15% for validation, and the same ration were applied to ANFIS and SVM. After reviewing the fitness statistical criteria such as correlation coefficient (R), and Root Mean Square Error (RMSE), it was observed that neural network presented an acceptable result compared to SVM and ANFIS models.

Quantitative prediction, groundwater, SVM, ANFIS, MLP

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Darini, T., & Jalalkamali, A. (2016). Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain. Environment Conservation Journal17(1&2), 85-94.

https://doi.org/10.36953/ECJ.2016.171210

Received: 21.01.2016

Revised: 25.03.2016

Accepted: 10.04.2016

First Online: 25.06. 2016

https://doi.org/10.36953/ECJ.2016.171210

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Publisher Name:  Action for Sustainable Efficacious Development and Awareness (ASEA)

Print : 0972-3099           

Online :2278-5124