利用机器学习进行洪水预报:综述

Parag R Ghorpade, A. Gadge, A. Lende, Hitesh Chordiya, G. Gosavi, A. Mishra, B. Hooli, Yashwant S. Ingle, N. Shaikh
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引用次数: 7

摘要

洪水是最常发生的自然灾害,造成人命损失、生计破坏,进而影响国民经济。如何有效地设计洪水预报系统,目前已有一些研究和新的思路。作者见证并阐述了最近向数据驱动的洪水预测方法的转变。使用气候参数历史数据训练的基于机器学习的模型在预测任务中越来越有用。本文的主要目的是展示使用机器学习算法的洪水预测领域的最新进展。作者回顾了一些用于洪水预报的突出算法,各种专业人员可以使用这些算法来开发他们的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flood Forecasting Using Machine Learning: A Review
Floods are the most frequently occurring natural disasters and result in loss of human life, destruction of livelihoods, which in turn, affects the national economies. There are several studies and novel modi operandi to design flood forecasting systems efficaciously. The authors witness and address the recent shift towards data-driven methods for flood prediction. The machine learning-based models trained using climatic parameters' historical data are increasingly useful for forecasting tasks. This paper's main objective is to demonstrate the recent advancements in the flood forecasting field using machine learning algorithms. The authors reviewed some prominent algorithms used for flood forecasting, which various professionals can use to develop their solutions.
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