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

Safavi HR, Malek Ahmadi K - J Environ Health Sci Eng (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

PEER results for all the stations
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Fig16: PEER results for all the stations

Mentions: If PEER is greater than zero, RBF-NN is then more efficient than MLP-NN. This indicator can analyze and examine the ability of the proposed model to minimize the prediction error. Equation 6 is adapted to present the efficiency of the RBF-NN model compared to MLP-NN. According to PEER values, RBF-NN shows greater improvements in Lenj, Mousian, and Chum-bridge stations over the MLP-NN. These improvements range from 7.91 to 22.03 %, but only in Lenj station, the MLP-NN is more efficient by about 70 %. However, both these models are generally usable since they both have low errors (Table 4 and Fig. 16).Table 4


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

Safavi HR, Malek Ahmadi K - J Environ Health Sci Eng (2015)

PEER results for all the stations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597443&req=5

Fig16: PEER results for all the stations
Mentions: If PEER is greater than zero, RBF-NN is then more efficient than MLP-NN. This indicator can analyze and examine the ability of the proposed model to minimize the prediction error. Equation 6 is adapted to present the efficiency of the RBF-NN model compared to MLP-NN. According to PEER values, RBF-NN shows greater improvements in Lenj, Mousian, and Chum-bridge stations over the MLP-NN. These improvements range from 7.91 to 22.03 %, but only in Lenj station, the MLP-NN is more efficient by about 70 %. However, both these models are generally usable since they both have low errors (Table 4 and Fig. 16).Table 4

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus