Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems

Safavi, Hamid R.; Chakraei, Iman; Kabiri-Samani, Abdorreza; Golmohammadi, Mohammad H.

doi:10.1007/s11269-013-0405-1

Cited by 28 publications

(7 citation statements)

References 29 publications

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“…The flow downstream the dam supplies water for agricultural, municipal, and industrial uses. The total river length spans over a route of 350 Km to end in Gavkhooni wetland [25]. …”

Section: Methodsmentioning

confidence: 99%

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

Safavi

Ahmadi

2015

J Environ Health Sci Engineer

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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.

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“…The flow downstream the dam supplies water for agricultural, municipal, and industrial uses. The total river length spans over a route of 350 Km to end in Gavkhooni wetland [25]. …”

Section: Methodsmentioning

confidence: 99%

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

Safavi

Ahmadi

2015

J Environ Health Sci Engineer

Self Cite

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“…To evaluate the performance of the deterministic and uncertainty multi-objective management model, the reliability, resiliency and vulnerability fuzzy criteria were considered. These fuzzy criteria are more accurate in assessing the operation status of water resources systems than their non-fuzzy state and have been developed by Safavi and Golmohammadi (2016). By summing up these criteria with the sustainability index, we can evaluate the water resources system in terms of sustainability of water resources and water supply demands.…”

Section: Performance Evaluation Criteria Of Developed Modelsmentioning

confidence: 99%

“…Finally, in order to evaluate the performance of OOPs in two deterministic and uncertainty models, fuzzy indicators for evaluating water resource systems that proposed by Safavi and Golmohammadi (2016) were used. These indicators show better the functional state of the water resources system in the limit values (threshold).…”

Section: Introductionmentioning

confidence: 99%

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

Tabari

Safari

2023

Soft Comput

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The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands.Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the nondeterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions.Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.

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“…In some cases, dynamic programming can not only make qualitative description and analysis but also give numerical solution by computer. 10 In this article, the dynamic planning method is used to solve the practical optimization of economic resources. Still there is an improvisation needs for efficiency and speed convergence, so in order solve such problems the multi-objective dynamic programming algorithm is proposed in this article and the procedure can be applied to production planning arrangements of different products of different enterprises.…”

Section: Introductionmentioning

confidence: 99%

“…Especially for discrete problems, dynamic programming becomes a powerful tool. In some cases, dynamic programming can not only make qualitative description and analysis but also give numerical solution by computer 10 . In this article, the dynamic planning method is used to solve the practical optimization of economic resources.…”

Section: Introductionmentioning

confidence: 99%

Dynamic planning method of economic operation optimization model

Jing

2020

Computational Intelligence

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Enterprise dynamic planning is a branch of operations research and multi-stage decision-making processes. This method can be used to solve multi-objective decision-making problems, thereby optimizing the overall effect of the system. Aiming at the dynamic programming method of economic operation optimization model, this article studies a multi-objective dynamic programming algorithm and improves the algorithm.Taking the optimization arrangement of enterprise production plan as an example, according to the production capacity, production cost and inventory cost of the enterprise, a dynamic planning method is adopted to establish a production plan arrangement model. The experimental results show that using the method proposed in this article to dynamically plan the economic operation optimization model can not only improve the convergence of the results but also improve its accuracy. This result solves the optimization problem of the enterprise's product production plan.

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Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems

Cited by 28 publications

References 29 publications

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

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

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

Dynamic planning method of economic operation optimization model

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