Developing reservoir evaporation predictive model for successful dam management

Allawi, Mohammed Falah; Ahmed, Mohammed Lateef; Aidan, Ibraheem Abdallah; Deo, Ravinesh C.; El-Shafie, Ahmed

doi:10.1007/s00477-020-01918-6

Cited by 18 publications

(3 citation statements)

References 43 publications

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“…A lot of greenhouse gases, such as carbon dioxide (CO 2 ), present in the nature and maintain hotness of the earth, locking the heat in the atmosphere. Enlarged reservoir evaporation (Allawi et al, 2020), amount change (Hidalgo et al, 2020) and changes in pattern of river runoff (Dandapat, Gnanaseelan & Parekh, 2020) will have numerous impacts on the hydroelectric power production due to changes in the climate. These cause bad effects on other energy sectors (Akram et al, 2020), financial effects (Ma, Rogers & Zhou, 2020) and system operation (Bento, 2020).…”

Section: Methodsmentioning

confidence: 99%

Application of improved fuzzy best worst analytic hierarchy process on renewable energy

Majumder

Balas

Paul

et al. 2021

PeerJ Computer Science

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In this work, a novel fuzzy decision making technique namely trapezoidal fuzzy Best-Worst method (fuzzy BWM) is developed which is based on Best-Worst method (BWM) and Trapezoidal fuzzy number. The real motive behind our work is to take a broad view of the existing fuzzy BWM based on triangular fuzzy number by trapezoidal fuzzy number. Also, we have presented a new hybrid MCDM technique called as Trapezoidal fuzzy Best Worst Analytic Hierarchy based on proposed trapezoidal fuzzy BWM and existing trapezoidal fuzzy Analytic Hierarchy Process (AHP). BWM approach is employed in evaluating the PV of considering criteria and trapezoidal fuzzy AHP is used to assess the local priority vale (PV) of considering alternatives (or indicators) of a decision problem. Moreover it used to identify the most significant alternative which is responsible for performance efficiency of a hydro power plant under climatic scenario. From the result, it is undoubtedly found that hydraulic had is most responsible indicator. Further, the CR (consistency ratio) value which is determined by our proposed trapezoidal fuzzy BWM is less than that of existing BWM and fuzzy BWM techniques. Finally, we have validated our result by comparative study, scenario analysis and sensitivity analysis.

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Section: Methodsmentioning

confidence: 99%

Application of improved fuzzy best worst analytic hierarchy process on renewable energy

Majumder

Balas

Paul

et al. 2021

PeerJ Computer Science

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“…In 2019, it was proposed to calculate the accurate spatial distribution of evaporation in the lake using a co-active neuro-fuzzy inference system (CANFIS) [56]. CANFIS was also used in another study for evaporation prediction in AHD [57]. From the literature, there are considerable differences between different methods of estimation regarding evaporation from the reservoir.…”

Section: Aswan High Dam Reservoir Case Studymentioning

confidence: 99%

Developing an Optimized Policy Tree-Based Reservoir Operation Model for High Aswan Dam Reservoir, Nile River

Goharian

Shaltout

Erfani

et al. 2022

Water

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The impacts of climate change on the Nile River and Grand Ethiopian Renaissance Dam (GERD) along with the increased water demand downstream suggest an urgent need for more efficient management of the reservoir system that is well-informed by accurate modeling and optimization of the reservoir operation. This study provides an updated water balance model for Aswan High Dam Reservoir, which was validated using combined heterogeneous sources of information, including in situ gauge data, bias-corrected reanalyzed data, and remote sensing information. To investigate the future challenges, the spatial distribution of the annual/seasonal Aswan High Dam Reservoir surface air temperature trends over the period from 1979 to 2018 was studied. An increase of around 0.48 °C per decade in average annual temperature was detected, a trend that is expected to continue until 2100. Moreover, a set of machine learning models were developed and utilized to bias-correct the reanalyzed inflow and outflow data available for Aswan High Dam Reservoir. Finally, a policy tree optimization model was developed to inform the decision-making process and operation of the reservoir system. Results from the historical test simulations show that including reliable inflow data, accurate estimation of evaporation losses, and including new regulations and added projects, such as the Toshka Project, greatly affect the simulation results and guide managers through how the reservoir system should be operated in the future.

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“…With the advancement of computer hardware and the improvement of soft computing techniques over the last three decades, data-driven ML techniques have been successfully used for the prediction of Epan [9]. Artificial neural networks (ANNs) [10], an adaptive neuro-fuzzy inference system (ANFIS) [11], least square support vector regression (LSSVM) [12], tree-based methods [13], a self-organizing map neural network (SOMNN) [14], multiple linear regression (MLR) [15], support vector machines (SVMs) [16], a classification and regression tree (CART) [17], an extreme learning machine (ELM) [18], gene expression programming [19], and a multivariate adaptive regression spline (MARS) [20] are among the methods that have been implemented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation

Adnan

Jaafari

Milan

et al. 2022

Water

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Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an adaptive neuro-fuzzy inference system (ANFIS) integrated with a metaheuristic optimization algorithm; i.e., particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris hawks optimization (HHO). The modeling data were acquired for the period between 1962 and 2001 (480 months) and were grouped into several combinations and incorporated into the hybridized models. The performance of the models was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R2), Taylor diagram, and Violin plot. The results showed that maximum temperature was the most influential variable for evaporation estimation compared to the other input variables. The effect of periodicity input was investigated, demonstrating the efficacy of this variable in improving the models’ predictive accuracy. Among the models developed, the ANFIS-HHO and ANFIS-WOA models outperformed the other models, predicting Epan in the study stations with different combinations of input variables. Between these two models, ANFIS-WOA performed better than ANFIS-HHO. The results also proved the capability of the models when they were used for the prediction of Epan when given a study station using the data obtained for another station. Our study can provide insights into the development of predictive hybrid models when the analysis is conducted in data-scare regions.

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Developing reservoir evaporation predictive model for successful dam management

Cited by 18 publications

References 43 publications

Application of improved fuzzy best worst analytic hierarchy process on renewable energy

Application of improved fuzzy best worst analytic hierarchy process on renewable energy

Developing an Optimized Policy Tree-Based Reservoir Operation Model for High Aswan Dam Reservoir, Nile River

Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation

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