Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

Mohammed, Sarah Jasim; Zubaidi, Salah L.; Ortega‐Martorell, Sandra; Al‐Ansari, Nadhir; Ethaib, Saleem; Hashim, Khalid S.

doi:10.1080/23311916.2022.2143051

Cited by 13 publications

(8 citation statements)

References 115 publications

(138 reference statements)

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“…It is 118 also hypothesized that the more objects participate in the sum, the smaller the relative approximation error will be. To test this hypothesis, modeling was carried out with the number of object models equal to three and four [34]. Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Investigation of linear models for control of water flow and temperature in a water supply system

Asset,

Mansurova,

Zhmud

et al. 2024

IJEECS

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In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.

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Section: Methodsmentioning

confidence: 99%

Investigation of linear models for control of water flow and temperature in a water supply system

Asset,

Mansurova,

Zhmud

et al. 2024

IJEECS

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“…This study focuses on the upstream area of the Three Gorges Reservoir, an essential part of the Yangtze River Basin, which is crucial for water resource management and flood control [30]. This study aims to select the best water-level-prediction algorithm among LSTM, BiLSTM, CNN-LSTM, and CNN-Attention-LSTM hybrid models [31]. By analyzing the predictive performance of each model in depth, the study aims to select the best waterlevel-prediction algorithm to improve prediction accuracy, thereby providing a scientific basis for reservoir operation management, flood warnings, and the rational allocation of water resources.…”

Section: Introductionmentioning

confidence: 99%

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

Li,

Zhang,

Zhang

et al. 2024

Water

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The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

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“…Additionally, articles on watershed-level predictions published between 2014 and 2021 were analysed by Mohammed et al [18]. Te study revealed that hybrid strategies outperform single methods for all cases, for instance, Ebtehaj et al [19], Imran et al [20], and Nguyen et al [21].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, knowledge gaps and promising new research directions of study related to the hybridisation of hybrid models need to be investigated. Also, Mohammed et al [18] reviewed the watershed-level prediction articles published from 2014 to 2021 and recommended employing all three data preprocessing approaches to improve original data quality and select the optimal predictors. Additionally, SSA is used for denoising data.…”

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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Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

Cited by 13 publications

References 115 publications

Investigation of linear models for control of water flow and temperature in a water supply system

Investigation of linear models for control of water flow and temperature in a water supply system

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

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

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