Comparative evaluation of imperialist competitive algorithm and artificial neural networks for estimation of reservoirs storage capacity

Emami, Somayyeh; Parsa, Javad

doi:10.1007/s13201-020-01259-3

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Due to this, Emami and Parsa (2020) try to predict the optimal reservoir storage capacity, using an evolutionary algorithm (inspired by imperialistic competition) along with an MLP model with a back propagation training technique, applied to Shaharchay Dam (Urmia Lake basin, Iran). According to the results, both models are satisfactory (with an RMSE of 0.041 and 0.045 for the imperialist competitive algorithm and the ANN model, respectively) (Emami and Parsa, 2020). Finally, another interesting research study is the one carried out by Sammen et al (2017) which used a generalized regression neural network (GRNN) to predict the peak outflow in the event of a possible dam failure.…”

Section: Mlp Modelmentioning

confidence: 96%

“…The hybrid MLP-GSA model showed a high efficacy over the other developed models and suggests, on the one hand, that it can be used in water resource management among other tasks. On the other hand, reservoir storage capacity determination is an important element in water resource management and planning, among others (Emami and Parsa, 2020). Due to this, Emami and Parsa (2020) try to predict the optimal reservoir storage capacity, using an evolutionary algorithm (inspired by imperialistic competition) along with an MLP model with a back propagation training technique, applied to Shaharchay Dam (Urmia Lake basin, Iran).…”

Section: Mlp Modelmentioning

confidence: 99%

“…According to Rashidi et al (2019), machine learning is an application of artificial intelligence (AI) that enables the automatic learning of computer systems, all based on experience without explicit programming. Machine learning techniques have been successfully applied in many hydrological applications (Le et al, 2019;Xiang et al, 2020;Kratzert et al, 2018;Rjeily et al, 2017;Guzman et al, 2017;Lee and Tuan Resdi, 2016;Taghi Sattari et al, 2012;Ghorbani et al, 2019;Emami and Parsa, 2020;Sammen et al, 2017) in the last few years. In this paper, several methodologies based on machine learning techniques are proposed for the time series forecasting of reservoir outflow.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of machine learning techniques for reservoir outflow forecasting

García-Feal

González-Cao

Fernández-Nóvoa

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Reservoirs play a key role in many human societies due to their capability to manage water resources. In addition to their role in water supply and hydropower production, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function, since extreme events have increased in the last few decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood damage will be key in the coming decades. Having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. When historical data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyzes the efficiency of different machine learning techniques to predict reservoir outflow, namely multivariate linear regression (MLR) and three artificial neural networks: multilayer perceptron (MLP), nonlinear autoregressive exogenous (NARX) and long short-term memory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques analyzed, the NARX approach was the option that provided the best estimations on average.

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Section: Mlp Modelmentioning

confidence: 96%

Section: Mlp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of machine learning techniques for reservoir outflow forecasting

García-Feal

González-Cao

Fernández-Nóvoa

et al. 2022

Nat. Hazards Earth Syst. Sci.

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“…According to Mahesh (2018) [48], ML includes algorithms and statistical models that computer systems use to resolve tasks without being expressly programmed. Numerous studies can be found in the literature in which machine-learning methods are successfully applied for hydrological purposes [49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting

Soria-Lopez,

Sobrido-Pouso,

Mejuto

et al. 2023

Water

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Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.

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“…Several models have been developed and applied for analysis and monitoring of water quality parameters (Ghosh et al 2015;Sen et al 2018;Adiat et al 2020;Emami and Parsa 2020). Traditional (deterministic and stochastic) models, such as statistical approaches and visual modelling, have been commonly used in literature (Sun and Gui 2015;Tziritis and Lombardo 2017;Chen et al 2018;Karami et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

2021

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Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

show abstract

Comparative evaluation of imperialist competitive algorithm and artificial neural networks for estimation of reservoirs storage capacity

Cited by 12 publications

References 30 publications

Comparison of machine learning techniques for reservoir outflow forecasting

Comparison of machine learning techniques for reservoir outflow forecasting

Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

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