Year: 2021
Type: article
Abstract: Weather forecasting is an essential component of different hydrological studies. This article compares the weather prediction performance of various machine learning models like k-nearest neighbours (KNN), Soil and Water Assessment Tools (SWAT), and Representative Concentration Pathway (RCP). KNN is more resistant to noisy data set and provides more reliable performance than RCP and SWAT models. We simulate temperature, precipitation, and wind speed using KNN, SWAT and RCP weather generators, and we compare the results with observed data. The analyses compare WP-KNN with state-of-the-art classification and prediction models. We also suggest a systematic forecasting methodology that uses an updated version of the KNN classification. Our extensive experimental modelling findings show that the proposed technique is much more effective in a noisy dataset.
Source: Data Science and Management
Authors: Vinay Kellengere Shankarnarayan, Hombaliah Ramakrishna
Institution: Dayananda Sagar College of Engineering
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W3186512974