Safavi, H. R., Malek Ahmadi, K., (2015) Prediction and Assessment of Drought Effects on Surface Water Quality Using Artificial Neural Networks: Case Study of Zayandehrud River, Iran, Journal of Environmental Health Science and Engineering 13 (1), 1
Hamid R. Safavi, Associate Professor, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran, email@example.com
Kian Malek Ahmadi ,Graduate Student, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran, firstname.lastname@example.org
Although drought impacts on water quantity are widely recognized, the impacts on water quality are less known. The Zayandehrud River basin in the west-central part of Iran plateau witnessed an increased contamination during the recent droughts and low flows. The river has been receiving wastewater and effluents from the villages, a number of small and large industries, and irrigation drainage systems along its course. What makes the situation even worse is the drought period the river basin has been going through over the last decade. Therefore, a river quality management model is required to include the adverse effects of industrial development in the region and the destructive effects of droughts which affect the river’s water quality and its surrounding environment. Developing such a model naturally presupposes investigations into pollution effects in terms of both quality and quantity to be used in such management tools as mathematical models to predict the water quality of the river and to prevent pollution escalation in the environment.
The present study aims to investigate electrical conductivity of the Zayandehrud River as a water quality parameter and to evaluate the effect of this parameter under drought conditions. For this purpose, artificial neural networks are used as a modeling tool to derive the relationship between electrical conductivity and the hydrological parameters of the Zayandehrud River. The models used in this research include multi-layer perceptron and radial basis function. Finally, these two models are compared in terms of their performance using the time series of electrical conductivity at eight monitoring-hydrometric stations during drought periods between the years 1997–2012. Results show that artificial neural networks can be used for modeling the relationship between electrical conductivity and hydrological parameters under drought conditions. It is further shown that radial basis function works better for the upstream stretches of the river while multi-layer perceptron is more efficient for the downstream stretches.
Keywords: Discharge Drought Temperature Electrical conductivity Artificial neural networks Multi layer perceptron Radial basis function