Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine lear.....

49 hits

The land use/land cover pattern of a region is an outcome of natural and socioeconomic factors and the utilisation by humans in time and space. This study aims to model the marine water quality using the relative impact of land use on marine water quality of selected river estuary between 2006-2013, Geographical Information System (GIS) and Cellular Automata (CA)-Markov method as a planning tool in evaluating Marine Water Quality Index (MWQI) were applied. The CA-Markov model revealed agricultural land use changes from 2006-2013 using land use land cover (LULC) in GIS as Setiu and Semerak River basins have 5.72% and 2.75%, respectively. The result indicated the impact of agricultural lands on MWQI, which is very low, according to projections of land use in 2020. Thus, the MWQI value in 2020 (Setiu 76.27 and Semerak 67.64) will be higher than MWQI mean value for 2006-2013. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024...

13 hits