An integrated framework for improving sea level variation prediction based on the integration Wavelet-Artificial Intelligence approaches. (June 2022)

• Record Type: Journal Article
• Authors: Alshouny, Ahmed; Elnabwy, Mohamed T.; Kaloop, Mosbeh R.; Baik, Ahmad; Miky, Yehia
• Is Part Of: Environmental modelling & software. Volume 152(2022)
• Publication Date: 2022-06
• Subjects: Sea level variation -- Artificial intelligence -- Neighborhood component analysis -- Deep learning neural network; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105399
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Modeling of Sea Level Variation (SLV) is a complicated phenomenon owing to multiple factors that happen at different spatial and temporal scales. Thus, this paper presents an innovative multistep interdependent framework based on Wavelet Transformation (WT) and Artificial Intelligence (AI) algorithms for SLV prediction. A wavelet time-frequency approach along with harmonic analysis is performed firstly to understand deeply SLV behavior. Then, Neighborhood Component Analysis (NCA) is applied for the Feature selection (FS) purposes. Finally, a Deep Learning Neural Network (DLNN) algorithm is utilized to predict precisely SLV based on a newly compacted dataset. The findings revealed the potential of the DLNN model over the Machine Learning models as it improves the SLV prediction accuracy by 23%. Additionally, the proposed DLNN model can predict SLV for a time horizon of three days with a correlation coefficient = 0.91 that can help in predicting SLV early for disaster management purposes. Highlights: WT and harmonic analysis are carried out to grasp profound SLV behaviour. EDA is a powerful tool to study the complicated relationship between climatic parameters and SLV. Neighborhood Component Analysis improves the performance of the developed SLV model results via removing redundant variables. DLNN outperformed ML algorithms for constructing SLV Predictive models for large datasets.