Prediction and assessment of drought effects on surface water quality using artificial neural networks: case study of Zayandehrud River, Iran

被引:0
|
作者
Hamid R. Safavi
Kian Malek Ahmadi
机构
[1] Isfahan University of Technology,Department of Civil Engineering
来源
Journal of Environmental Health Science and Engineering | / 13卷
关键词
Discharge; Drought; Temperature; Electrical conductivity; Artificial neural networks; Multi layer perceptron; Radial basis function;
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中图分类号
学科分类号
摘要
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.
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