Publication: Improving sea level prediction in coastal areas using machine learning techniques
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Date
2024
Authors
Latif S.D.
Almubaidin M.A.
Shen C.G.
Sapitang M.
Birima A.H.
Ahmed A.N.
Sherif M.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Ain Shams University
Abstract
The objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data determined by the machine learning model's performance. Thirteen models were trained precisely and properly throughout the machine learning process. The results showed that SVM models provide good performance during the training process and attained relatively poor performance during testing process. On the other hand, the KNN model showed consistent performance for both training and testing process. Regarding the effectiveness of different kernels of the SVM algorithm, the Radial Basis Function (RBF) kernel is the most suitable, which provides the finest analysis for the sea level rise dataset and acceptable values for RSME, MAE, and R2. ? 2024 THE AUTHORS
Description
Keywords
Coastal zones , Learning systems , Motion compensation , Nearest neighbor search , Radial basis function networks , Sea level , Coastal area , Flood modeling , K-near neighbor , K-nearest neighbour models , Machine learning techniques , Machine-learning , Support vector machine , Support vectors machine , Testing process , Training process , Support vector machines