Flood Prediction based on Weather Parameters in Jakarta using K-Nearest Neighbours Algorithm

Abstract

Flooding is a difficult and common hazard in Indonesia, particularly in Jakarta during the rainy season. Floods have been the subject of several endeavours, ranging from discovering the causes to reducing their impacts. Floods cause significant damage to infrastructure, the social economy, and human lives. The government continues to create reliable flood risk maps and plans for long-term flood risk management. According to data from Jakarta Flood Monitoring, 12 sub-districts and 26 urban villages were hit by floods each year between 2016 and 2020, with an average flood length of nearly 2 days. The flood tendency in Jakarta decreased from 2018 to 2019, but increased in 2020. Floods are produced by a variety of reasons, including weather, geography, and human actions such as deforestation. Strong flood prediction is required for disaster management, however this might be difficult owing to changing weather conditions. This study focuses on flood prediction in Jakarta based on weather parameters utilising machine learning techniques to provide accurate and real-time predictions. K-Nearest Neighbours (KNN) is an algorithm employed to forecast the areas that will encounter the consequences of floods. The outcomes of this research with the value of k=2 to k=9 obtained the best performance values at k=7, where the level of accuracy reaches 92.25%, 88.89% precision, 92.25% recall, and F1-measure of 89.52%. The integration of machine learning algorithms which encompasses multiple weather variables provides significant utility in comprehensive flood predictions and early warning systems in flood disaster mitigation.