Abstract
Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that artificial neural network (ANN) is a suitable tool for quality modeling of reservoir of dam and increment and decrement of nutrients in trend of eutrophication. Therefore, ANN is a suitable tool for quality modeling of reservoir of dam.
Highlights
Eutrophication and algal blooms are serious problems in many lakes and reservoirs (Cuneyt Karul 2000)
Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature
The results indicated that artificial neural network (ANN) is a suitable tool for quality modeling of reservoir of dam and increment and decrement of nutrients in trend of eutrophication
Summary
Eutrophication and algal blooms are serious problems in many lakes and reservoirs (Cuneyt Karul 2000). The trophic state indexes (TSI), especially the Carlson-type TSIs, have been proposed by some researchers in order to characterize the multidimensional nature of eutrophication and to effectively eliminate the subjective labeling associated with the use of oligotrophic, mesotrophic and eutrophic states as indicators (e.g., Carlson 1977; Walker 1979; Porcella et al 1980; Swanson 1998; Aizaki et al 1981; Jin et al 1990; Xu 2008) These TSIs offer a 0–100 scale providing continuous numerical classes of lake trophic states and a rigorous foundation for quantitative studies of the underlying causes of eutrophication. Carlson Trophic state index expressed by empirical Eqs. (1–3) and results were analyzed based on Table 1 as follows: TSI 1⁄4 60 À 14:43 LN ðSDÞ ð1Þ
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