Machine Learning Tools and Techniques for Prediction of Droughts

被引：0
|
作者：
Nitwane, Rashmi [1 ]
Bhagile, Vaishali D. [2 ]
Deshmukh, R. R. [2 ]
机构：
[1] Dr BAM Univ, CS & IT, Aurangabad, India
[2] Dr BAM Univ, CS & IT Dept, Aurangabad, India
来源：
5TH WORLD CONGRESS ON DISASTER MANAGEMENT, VOL. 2: Nature and Human Induced Disasters | 2023年
关键词：
convolutional neural network; generative adversarial network; markov chain models; recursive multistep neural network; Deep belief network; Empirical Mode Decomposition; CLASSIFICATION; NETWORK;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Due to climate change and global warming there is increase in occurrences of natural disasters in recent times. Droughts are one of the worst disasters that has long lasting effects on the food security and water resources of the country. The onset of drought is slow and recovering from a drought situation takes considerable amount of time and resources. Development of drought prediction tools is still in early stages. Since drought prediction is dependent on various climatic factors therefore there is need for integration of various technologies. With the advent of technologies like data science, machine learning and artificial intelligence prediction models are developed for creating early warning systems so as to plan ahead and reduce the loss incurred by this natural disaster. The existing prediction tools have not achieved required accuracy for proper mitigation planning. In this article we discuss the advantages and challenges of integrating machine learning tools with remote sensing techniques using the climatic data observed over the area for particular time span.
引用
收藏
页码：273 / 277
页数：5
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