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A Pluvial Flood Detection Model Using Machine Learning Techniques and Simulate The Flow of Water

机译：利用机器学习技术模拟水流的雨洪检测模型

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摘要

Floods are one of the worst natural calamities out there to befall their oppressive destruction on life as a whole. Floods are caused by a strong resurgence of indomitable water bodies that maneuver beyond their natural limits to cause extensive quantum of destruction to life and infrastructure. Flooding of a particular area is dependent on various factors like average rainfall, precipitation, proximity to water bodies, vegetation to name a few. This project aims to create a dataset based on the following factors and test the data using different models. Linear Regression, Support Vector Machine, Decision tree, and Random forest were the classification models utilized and the accuracy of each was testified. The outcome can be effectively utilized by meteorological authorities and disaster management teams to necessitate action in areas about to experience a bout of the flood. In this project, our objective is to use multiple algorithms to test their flood detection success rate and simulate the flow of water in a designated area.
机译：洪水是其中最严重的自然灾害之一，洪水对整个生命造成了压迫性破坏。洪水是由顽强的水体大量兴起造成的，这些水体的活动超出其自然极限，对生命和基础设施造成广泛破坏。特定地区的洪水取决于各种因素，例如平均降雨量，降水量，与水体的接近程度，植被等。该项目旨在基于以下因素创建数据集，并使用不同的模型测试数据。使用线性回归，支持向量机，决策树和随机森林作为分类模型，并验证了每种模型的准确性。气象部门和灾难管理团队可以有效利用结果，以便在即将遭受洪灾的地区采取行动。在此项目中，我们的目标是使用多种算法来测试其洪水检测成功率并模拟指定区域中的水流。

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