Forecasting water level of Glacial fed perennial river using a genetically optimized hybrid Machine learning model

Imran, Mirza; Khader, P. Sheikh Abdul; Rafiq, Mohammd; Rawat, Kishan Singh

doi:10.1016/j.matpr.2021.02.256

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Imran (Imran et al, 2021) inspected the performance of an adaptive neuro-fuzzy inference system (ANFIS) model that coupled with a genetic algorithm to forecast the water levels of the Jhelum River, India. Two meteorological stations' temperature and precipitation data were employed to train the model to simulate the river's water level.…”

Section: The Genetic Algorithm (Ga)mentioning

confidence: 99%

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

et al. 2022

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The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

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Section: The Genetic Algorithm (Ga)mentioning

confidence: 99%

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

et al. 2022

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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]. Multiple studies recommended applying the hybrid models in water level forecasting, for example, Ghorbani et al [22], Wang et al [23], Zhu et al [24], and Dat et al [25].…”

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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September 2014 Floods in Kashmir Himalaya—Impacts and Mitigation Strategy

Rafiq

Kesarkar

Derwaish

et al. 2022

Disaster Management in the Complex Himalayan Terrains

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Forecasting water level of Glacial fed perennial river using a genetically optimized hybrid Machine learning model

Cited by 4 publications

References 41 publications

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

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

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

September 2014 Floods in Kashmir Himalaya—Impacts and Mitigation Strategy

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