Predicting reservoir volume reduction using artificial neural network

Iraji, Hadi; Mohammadi, Mirali; Shakouri, Behzad; Meshram, Sarita Gajbhiye

doi:10.1007/s12517-020-05772-2

Cited by 15 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the application of ANN with a single model, ANN could also work well with other algorithms. Hadi et al combined ANN with MLP [20,23]. ANN was also applied together with Molecular Dynamic (MD) [21,35].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Zhou

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Previous studies have demonstrated CO2 absorption by soils in arid regions, where the absorbed CO2 is conjectured to be finally sequestrated in the “subterranean ocean”—groundwater. This study compares environmental controls of ocean CO2 concentration (surface ocean pCO2) and quasi-ocean CO2 concentration (deep-soil pCO2). We aim to explore the latent relationship, both linear and nonlinear between the environmental variables, and CO2 concentration, utilizing two intelligent algorithms—the partial least linear regression (PLSR) algorithm and the artificial neural network (ANN) algorithm. For quasi-ocean CO2 concentration, RPD <1.4 and R2 <40%. While for ocean CO2 concentration, RPD >1.4 and R2 is 99.7%. Linear relationships between the considered environmental controls and ocean CO2 concentration are proved; however, there is no evident relationship between most of the considered environmental controls and quasi-ocean CO2 concentration. Groundwater level is proved to be a relatively important environmental control on the quasi-ocean CO2 concentration, suggesting groundwater discharge/recharge as a significant modulator of soil CO2 absorption in arid regions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Finally, for each PLSR and ANN model in the present study, if RPD >2, then we will conclude that the model has a good ability for prediction; if the RPD is less than 1.4, then we will claim that the model is unable to make good estimation. is rule has been proposed and widely recognized in some previous studies [22,23].…”

Section: Plsr and Annmentioning

confidence: 99%

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Zhou

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Latif et al (2021) he studied the reservoir water balance simulation model using machine learning algorithm. Iraji et al (2020) estimated the reduction in reservoir volume using an artificial neural network. Paul et al ( 2019) a comparative study of wavelet transforms and MLR, KNN (K-Nearest Neighbors), ANN and ANFIS models in monthly flow estimation.Other studies on artificial intelligence in the field of hydraulics Ozel 2020).…”

Section: Introductionmentioning

confidence: 99%

Estimating Dam Reservoir Level Change of Istanbul Alibey Dam with The Fuzzy SMRGT Method

Üneş

Taşar

2022

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

View full text Add to dashboard Cite

Accurate estimation of the dam reservoir level in a dam reservoir is very important for the planning and operation of water structures. In this study, monthly dam reservoir level data between 1989 and 2020 obtained from the State Hydraulic Works (DSI) was used to estimate the monthly dam reservoir level change. For the monthly dam reservoir level estimation, it has been tried to be estimated using the Simple Membership Functionsand Fuzzy Rules Generation Technique (Fuzzy-SMRGT), Artificial Neural Network (ANN) and the classical Multiple Linear Regression (MLR) methods. Alibey Dam located in Sultangazi district of Istanbul was chosen as the study area. The monthly evaporation, water entering into the lake, consumption of drinking water and amount of water discharged from the dam amounts were used to estimate the monthly Alibey Dam reservoir average dam reservoir level. The model results were compared with the actual observation dam reservoir levels. When the results were evaluated, it was observed that the models were successful in estimating the dam reservoir level and gave results close to each other

show abstract

“…The major advantage of such SC techniques is that these models are fully nonparametric and do not require a priori concept of the relations between the input variables and the output data (Gocic ´et al 2015;Fahimi et al 2017). Various researchers have used ANNs for hydrologic studies including time series predictions of runoff or streamflow (Hsu et al 1995;Govindaraju 2000;Rajaee et al 2009Rajaee et al , 2010Melesse et al 2011;Lafdani et al 2013;Khan et al 2018;Meshram et al 2019aMeshram et al , 2019bMeshram et al , 2020Meshram et al , 2021aMeshram et al , 2021bMeshram et al , 2021cIraji et al 2020). Sudheer et al (2003) used radial-based neural networks for partial weather data; Trajkovic (2005) used radial-based neural networks using temperature-based models; Kisi (2007) applied a neural computing technique using climatic data; and Aytek (2008) applied a co-active neuro-fuzzy interpretation system.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

Meshram

Meshram²,

Pourhosseini

et al. 2021

Soft Comput

Self Cite

View full text Add to dashboard Cite

The prediction of river sediment load is an essential issue in water resource engineering problems. In this study, artificial neural network employed in order to estimate the daily sediment load on rivers. Two different algorithms, multi-layer perceptron (MLP) and hybrid MLP-FFA (MLP integrated with the FFA) were used for this purpose in the Lake Mahabad, Iran. For this purpose, nine different scenarios are considered as inputs of the models. Performance of selected models was evaluated on basis of performance criterion namely root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ) for choosing best fit model. The results indicated that the new hybrid model MLP-FFA is successful in estimating sediment load with high accuracy as compared with its alternatives with RMSE = 2018 ton/day, MAE = 1698 and R 2 = 0.95, which were much lower than those of MLP-based model with RMSE = 3044 ton/day, MAE = 2481 and R 2 = 0.90. The results of the present study confirmed the suitability of proposed methodology for precise modeling of suspended sediment load.

show abstract

Predicting reservoir volume reduction using artificial neural network

Cited by 15 publications

References 52 publications

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Estimating Dam Reservoir Level Change of Istanbul Alibey Dam with The Fuzzy SMRGT Method

A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

Contact Info

Product

Resources

About