The Relationship between Surface Water Quality and Watershed Characteristics

Mehdi Vafakhah

doi:10.31031/eaes.2018.01.000514

Abstract

Environmental Analysis & Ecology Studies The Relationship between Surface Water Quality and Watershed Characteristics Mehdi Vafakhah* Faculty of Natural Resources, Tarbiat Modarres University, Iran *Corresponding author: Mehdi Vafakhah DR, Associate Professor in Water Resources and Hydrology Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modarres University, P.O.Box 46417-76489, Noor, Mazandaran Province, Iran Submission: December 16, 2017; Published: February 27, 2018 DOI: 10.31031/EAES.2018.01.000514 ISSN: 2578-0336 Volume1 Issue3

Highlights

Clean water is an essential prerequisite for environmental protection and economic development, political, social and cultural development of a country [1]
Water quality data, including: total dissolved solids (TDS), sodium absorption rate (SAR), electrical conductivity (EC), Na+, Cl, CO32, K+, Mg2+, Ca2+, pH, HCO3- and SO42- from 63 sampling sites for the period October 2010 to September 2011 collected by Iran Water Resources Research Center (IWRRC) have been used for this study (Table 1)
The purpose of the current study was to model the relationship between water quality parameters and characteristics of the watersheds of rivers using Adaptive Neuro-Fuzzy Inference System (ANFIS) in Mazandaran province

Summary

Introduction

Clean water is an essential prerequisite for environmental protection and economic development, political, social and cultural development of a country [1]. Inappropriate use of chemicals in agriculture, led to increasing pollution of water resources that are passing through the towns and villages which and were infected enough. Several indirect methods to simulate natural systems, estimates more accurate, more comprehensive and more complex calculations using a computer has been invented. One of these methods is modelling or simulation. There are many models for predicting water quality parameters including white box and black box models Among these use of the statistical methods to predict water quality parameters, in terms of taking into account the characteristics of the watershed and lack of complexity of white-box models has attracted the researchers [4]. There has been an increasing interest in intelligence models e.g. Artificial Neural Network (ANN), Adaptive Neuro-fuzzy Inference System (ANFIS), Fuzzy Logic and Genetic Algorithm for systems control [5,6]

Objectives

Methods

Conclusion