Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.

翻译：洪水是自然界最灾难性的灾害之一,对人的生命、农业、基础设施和社会经济系统造成了不可逆和巨大的破坏。已经进行了几项关于洪水灾难管理和洪水预报系统的研究。准确预测洪水的发生和实时发展是具有挑战性的。为了估计大面积地区的水位和速度,有必要将数据与计算要求很高的洪水传播模型结合起来。本文件旨在减少这一自然灾害的极端风险,并通过利用不同的机器学习模型提供洪水预测来帮助提出政策建议。这项研究将使用二进制物流倒退、K-Nearest Neearbbor(KNN)、支持Vectictor分类(SVC)和决定树分类仪来提供准确的预测。根据结果,将进行比较分析,以了解哪种模型能提供更好的准确性。