Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

Allawi, Mohammed Falah; Othman, Faridah; Afan, Haitham Abdulmohsin; Ahmed, Ali Najah; Hossain, Shabbir; Fai, Chow Ming; El-Shafie, Ahmed

doi:10.3390/w11061226

Cited by 36 publications

(19 citation statements)

References 21 publications

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“…TA B L E 10 The comparison of the obtained results The results of this research have been compared with the results of Allawi et al 60 Table 10 shows the comparison of the obtained results.…”

Section: Estimation Of the Reservoir Water Evaporationmentioning

confidence: 93%

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Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

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One of the most sensitive factors affecting hydropower generation is climate change.The objective of this study is to forecast the hydropower generation under the influence of climate change for the next 50 years. For this purpose, the GFDL-CM3 model is used under three scenarios: RCP2.6, RCP4.5, and RCP8.5 to predict precipitation and temperature. Any change in the inlet flow to the turbine will cause changes in the hydropower output. Therefore, the more accurately the flow is estimated, the hydropower generation is forecasted more accurately. In this research, a hydrological-neural network hybrid model for flow prediction is presented in which the Improved Sparrow Search Algorithm (ISSA) has been used. The results forecasting climate parameters showed that the average annual rainfall, runoff, and evaporation have a downward trend compared to the control period. The minimum and maximum annual temperatures have an increasing trend compared to the control period. To evaluate the hydropower generation Aras dam, the HEC-Ressim reservoir model is utilized. By comparing the average annual hydropower with the control period, it is known that the power generation will decrease in future years. The average annual electricity generated by hydropower under the scenario RCP2.6 and RCP4.5 and RCP8.

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Section: Estimation Of the Reservoir Water Evaporationmentioning

confidence: 93%

“…Reservoir simulation models operate based on the relationships in hydraulic structures, the hydrological status of the catchment, the environmental effects of the area, and other factors. 60 The HEC-ResSim model is used to estimate the potential water release rate and reservoir height to…”

Section: Simulation Reservoirmentioning

confidence: 99%

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

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“…For this cause, the water scarcity level was determined. The concept of volumetric (Rv) and periodic (Rp) reliability from [33] is incorporated into Equations ( 18) and (19). Reliability is the key index of the efficiency check of a model in order to meet the goals for a reservoir optimization model, as explained by Zio [34].…”

Section: Reliability (Rv and Rp)mentioning

confidence: 99%

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

et al. 2021

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In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.

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“…where Od is the output decision, ωj and ω0 represent the connection weights, and h is the hidden layer. The hidden layers are also connected to the output layers through a neural connection which holds the output weights [33,[71][72][73][74][75]. Initially, the weights of the connections hold random values until they intersect another connection-a phase in which they are multiplied by the associated weights and that intersection [34].…”

Section: Multilayer Perceptron Neural Network (Mlp)mentioning

confidence: 99%

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Popa

Peptenatu

Drăghici

et al. 2019

Water

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The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

Cited by 36 publications

References 21 publications

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

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