Flood Detection Using Gradient Boost Machine Learning Approach
Flood Detection Using Gradient Boost Machine Learning Approach
复制标题
使用梯度提升机器学习方法进行洪水检测
DOI：
10.1109/iccike47802.2019.9004419
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
T. Sasipraba
中科院分区：
文献类型：
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作者：
A. Y. Felix;T. Sasipraba
洪水是一种自然灾害，由于水体容量的恢复远远超出其自然极限导致溢出，导致旱地被水淹没。洪水最常见的原因是过量的降水和径流，导致邻近的土地被水淹没，给人类生命和基础设施造成巨大损失，包括破坏建筑物、桥梁、供电网络、瘫痪交通和给人民带来经济困难。多年来，已经采取了多种措施来预先确定洪水预警，这些措施是利用传感器技术和主动监测参数来实施的。这导致了大量数据集的创建，这些数据集可用于未来的目的，并且随着机器学习和智能机器概念的复兴所预示的数据分析技术的可用性，数据集可以直接用于允许算法直接从收集的数据中“学习”，并在此基础上创建一个预定的方程作为模型来帮助预测未来的结果。在所提出的方法中，我们提出了一种使用梯度提升算法的洪水检测机制，该算法将用于对数据集进行分类并对其执行回归，以从我们将用于训练它的数据集中产生最佳结果，从而创建基于决策树的弱预测模型。此后，结果可用于向有关当局展示，后者可以采取先发制人的行动来应对威胁。这种方法的开发是为了更适合提供高精度预测的目的，并且还采用遥感和传感器技术等各种其他技术来开发训练模型所需的准确数据集。
Floods are a natural calamity which leads the dry land to be submerged by water due to a resurgence of a waterbody capacity which goes well beyond its natural limits leading to an overflow. Floods are most commonly caused by excessive precipitation and runoffs which lead the adjoining land areas to be submerged by water which causes huge loss to human lives and infrastructure, which includes damaging buildings, bridges, power supply network and crippling the transportation and bringing economic hardships on the people. Over the years, multiple measures have been taken to predetermine flood warnings which have been implemented using sensor technology and active monitoring of the parameters. This had led to the creation of a wide number of data-sets which can be employed for future purposes and with the availability of data analytics techniques heralded by the resurgence of Machine Learning and the concept of Intelligent Machines, the datasets can be directly employed to allow algorithms to "learn" directly from the collected data and based upon this, create a predetermined equation as a model to help predict future outcomes. In the proposed method, we propose a Flood Detection mechanism using the Gradient Boost Algorithm which will be used to classify the data sets and perform regression on it to produce the best outcomes from the datasets we will use to train it, to create a weak prediction model based on a Decision Tree. The outcome can henceforth be used to display it to the concerned authorities who can employ preemptive actions to tackle the threat. This approach is developed to be better suited in such ends providing predictions with high accuracy and additionally employs various other technologies like Remote Sensing and Sensor Technology to develop accurate datasets required to train the model.