Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Larrea, Pablo Páliz; Zapata‐Ríos, Xavier; Parra, Lenin Campozano

doi:10.3390/w13152011

Cited by 13 publications

(6 citation statements)

References 52 publications

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“…Tese models' features can receive a substantial quantity of data and can be applied to numerous climatic parameters and other hydrological boundary factors [8]. According to the published literature survey, multiple AI models for water level modelling have been built, including artifcial neural network (ANN) [9], adaptive neuro-fuzzy inference (ANFIS) [10], support vector machine (SVM) [11], and random forests (RFs) [12]. Te advantages and disadvantages of the models mentioned above (main ML models) are covered according to diferent topics of hydrology felds, such as drought [13] and water quality [14].…”

Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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“…Machine learning (ML) models have been discovered to be extremely effective in modelling nonlinear systems and prediction environmental phenomena [10]. There are many types of ML models, such as support vector machine (SVM) [11], adaptive neuron-fuzzy inference (ANFIS) [12], genetic programming (GP) [13], and artificial neural network (ANN) [14].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, various research in water level forecasting [12,19,[32][33][34] recommend using climatic factors for forecasting WLs to enhance prediction precision, so climate factors will be involved as predictors in this study.…”

Section: Introductionmentioning

confidence: 99%

Hybridisation of artificial neural network with particle swarm optimisation for water level prediction

Mohammed,

Zubaidi

2023

ejuow

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Accurate water level (WL) prediction is essential for the efficient management of various water resource projects. The creation of a reliable model for WL forecasting is still a difficult task in water resource management. This study applies an artificial neural network (ANN) integrated with the particle swarm optimisation algorithm (PSO-ANN) for simulating monthly WL of the Tigris River in Alkut City, Iraq. Data pre-treatment methods are utilised for improving raw data quality and detect the optimal predictors. Monthly WL and climatic variables from 2011 to 2020, were used to construct and validate the proposed technique. The results showed that singular spectrum analysis (SSA) is a high-performance technique for denoising time series. The PSO-ANN model produces good results coefficient of determination (R2) of 0.85.

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“…The physically based models are mainly based on hydrodynamic models with Saint Venant equations as the governing equations to simulate one-dimensional channel flow, and the use of these models is somewhat limited because they require complete information about the study area [2,3]. Machine learning models mainly include the relevance vector machine (RVM) model [4], grey model(1,1) (GM (1,1)) model [5], multiple linear regression model [6], and neural network model [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The first three models are applicable to complex prediction situations, but their prediction accuracy is not sufficiently high.…”

Section: Introductionmentioning

confidence: 99%

A Novel GRA-NARX Model for Water Level Prediction of Pumping Stations

Liu

Lei

et al. 2022

Water

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It is necessary but difficult to accurately predict the water levels in front of the pumping stations of an open-channel water transfer project because of the complex interactions among hydraulic structures. In this study, a novel GRA-NARX (gray relation analysis—nonlinear auto-regressive exogenous) model is proposed based on a gray relation analysis (GRA) and nonlinear auto-regressive exogenous (NARX) neural network for 2 h ahead for the prediction of water levels in front of pumping stations, in which an improved algorithm of the NARX neural network is used to obtain the optimal combination of the time delay and the hidden neurons number, and GRA is used to reduce the prediction complexity and improve the prediction accuracy by filtering input factors. Then, the sensitivity to changes of the training algorithm is analyzed, and the prediction performance is compared with that of the NARX and GRA-BP (gray relation analysis back-propagation) models. A case study is performed in the Tundian pumping station of the Miyun project, China, to demonstrate the reliability and accuracy of the proposed model. It is revealed that the GRA-NARX-BR (gray relation analysis—nonlinear auto-regressive exogenous—Bayesian regularization) model has higher accuracy than the model based only on a NARX neural network and the GRA-BP model with a correlation coefficient (R) of 0.9856 and a mean absolute error (MAE) of 0.00984 m. The proposed model is effective in predicting the water levels in front of the pumping stations of a complex open-channel water transfer project.

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Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Cited by 13 publications

References 52 publications

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Hybridisation of artificial neural network with particle swarm optimisation for water level prediction

A Novel GRA-NARX Model for Water Level Prediction of Pumping Stations

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