Improving sea level prediction in coastal areas using machine learning techniques
Document Type
Article
Publication Date
9-1-2024
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.
Keywords
Machine learning, Support Vector Machine (SVM), k -Nearest Neighbors (kNN), Flood modeling, Coastal areas
Divisions
sch_civ
Publication Title
Ain Shams Engineering Journal
Volume
15
Issue
9
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS