Grid Search Based SVM Approach for Sea Level Rise

Autor:	O. Archana Sajith, S. Sithara, Santosh G. Thampi, S. K. Pramada
Rok vydání:	2020
Předmět:	Support vector machine Sea level rise Hyperparameter optimization Environmental science Data mining computer.software_genre computer
Zdroj:	IOP Conference Series: Earth and Environmental Science. 581:012032
ISSN:	1755-1315 1755-1307
DOI:	10.1088/1755-1315/581/1/012032
Popis:	Sea level rise is one of the most damaging impacts of climate change. Rising sea levels leads to loss of coastal wetlands, coastal flooding, degradation of coastal ecosystem, sinking of islands and general loss of quality of life. Countries which are located in low-lying areas as well as small islands are concerned that their land areas would be decreased due to inundation and coastal erosion. In this paper, a hybrid wavelet and grid search based support vector machine was used for statistical downscaling of sea level using climatic variables. The results of the hybrid model were compared with the observed sea level data and the results indicate that the hybrid wavelet and grid search based support vector machine can be used for future sea level projection. Thus the effect of sea level rise on low lying island can be studied with the model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b8c7533e29fbd99c57efc9a305e79f4e https://doi.org/10.1088/1755-1315/581/1/012032 Zobrazit plný text záznamu