Empirical Mode Decomposition Couple with Artificial Neural Network for Water Level Prediction

Loh, Eng Chuen; Ismail, Shuhaida Binti; Khamis, Azme

doi:10.13189/cea.2019.071403

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Monsoon flood is triggered by prolonged heavy widespread rain leads that to landslide (Piper et al, 2016). The east coast and southern part of Peninsular Malaysia, over the years, have been facing monsoonal flood and have been subjected to many research in predicting the rainfall to mitigate the flood risks (Ata, 2018;Loh et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

Shaaban¹,

Hassan²,

Mostafa³

2021

Journal of Computer Science

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Flood is one of the most destructive phenomena all over the world. Because the flooding uncertainties and the urgency to prepare for disaster management, three specific technique approaches are compared in this study to predict the flood occurrence based on historical rainfall data. The study involved the rainfall data in Kemaman, Terengganu between 2017 and 2018 extracted from the official portal of the state of Terengganu. The dataset covers daily rainfall reading between January to December of the particular year in millimeter (mm) per day along with flood risks occurrence. This prediction experiment will be conducted using three variations algorithms, which are Decision Tree, Naive Bayes and Support Vector Machine. The comparison using three different algorithms was used to define the best algorithms that work with historical rainfall datasets to predict flood in terms of accuracy, precision, recall and F1-score. In the future, the prediction results are hoped to alert government authorities to make an early strategy to handle flood problems in Malaysia by analyzing the rainfall pattern.

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Section: Introductionmentioning

confidence: 99%

Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

Shaaban¹,

Hassan²,

Mostafa³

2021

Journal of Computer Science

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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

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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Double decomposition with enhanced least-squares support vector machine to predict water level

Someetheram,

Marsani,

Mohd Kasihmuddin

et al. 2024

Journal of Water and Climate Change

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As global climates undergo changes, the frequency of water-related disasters rises, leading to significant economic losses and safety hazards. During flood events, river water levels exhibit unpredictable fluctuations, introducing considerable noise that poses challenges for accurate prediction. A prediction of water level by using existing water level data makes a major contribution to forecasting flood. Enhanced least-squares support vector machine (ELSSVM) is utilized by integrating an additional extra bias error control term. In this study, least-squares support vector machine (LSSVM) and ELSSVM optimized by the genetic algorithm (GA) were chosen to be compared with the help of data decomposition methods to improve daily water level prediction accuracy. Double empirical mode decomposition (DEMD) will be integrated with LSSVM and ELSSVM. Thus, the models are named LSSVM-GA, ELSSVM-GA, empirical mode decomposition (EMD)-LSSVM-GA, EMD-ELSSVM-GA, DEMD-LSSVM-GA, and DEMD-ELSSVM-GA. The proposed models are used in forecasting the water level of Klang River in Sri Muda, Malaysia. The behavior proposed models are investigated and compared based on several performance metrics such as root-mean-square error (RMSE) and squared correlation coefficient (R2). The results demonstrated that the DEMD-ELSSVM-GA model outperformed the other models based on the performance analysis in forecasting the water level with RMSE = 0.2536 m and R2 = 0.8596 for testing data that indicate the forecasting accuracy.

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Empirical Mode Decomposition Couple with Artificial Neural Network for Water Level Prediction

Cited by 3 publications

References 25 publications

Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

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

Double decomposition with enhanced least-squares support vector machine to predict water level

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