Advancing Flood Prediction: Leveraging Machine Learning for Accurate Prediction

Abstract

Abstract: Floods, a force of nature that cannot be escaped, relentlessly strike without discrimination. Every nation, to shield itself from this calamity, strives to make adequate preparations. Countless countries endure the devastation of floods annually, suffering immeasurable losses in various aspects such as finance, environment, and human lives. Regrettably, these losses are irreparable, no matter the efforts made. Amongst the different types of floods, flash floods pose the gravest danger as they possess both the destructive force of a flood and an astonishing velocity. These flash floods occur when intense rainfall surpasses the soil's ability to absorb water. They also occur when water fills dry creeks or streams or when sufficient water accumulates to breach riverbanks, resulting in rapid surges in a short period. Flash floods can materialize within minutes after triggering incidents, leaving little time for public warning and protection. As one facet of artificial intelligence, machine learning greatly contributes to the progression of predictive systems, offering enhanced performance and cost-effective solutions. Flood prediction must be accurate and fast to mitigate its damage. Machine learning algorithms have gained popularity for flood prediction recently because of their capacity to analyse massive volumes of data and find subtle patterns. This article presents an overview of flood prediction technology utilizing machine learning, encompassing the diverse algorithms employed, the benefits and drawbacks of this technology, and the challenges that lie ahead along with potential future developments.