Comparing Machine-Learning Models for Drought Forecasting in Vietnam’s Cai River Basin
Comparing Machine-Learning Models for Drought Forecasting in Vietnam’s Cai River Basin
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DOI：
10.15244/pjoes/80866
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发表时间：
2018-07
影响因子：
1.8
通讯作者：
Zhen Nan Liu;Qiongfang Li;L. Nguyen;Guimei Xu
中科院分区：
文献类型：
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作者：
Zhen Nan Liu;Qiongfang Li;L. Nguyen;Guimei Xu
干旱发生在世界各地，对人们的影响超过任何其他主要自然灾害，特别是在农业领域。需要有一个有效和及时的监测系统来减轻干旱的影响。同时，极端学习机（ELM）、在线序列极端学习机（OS-ELM）和自适应进化极端学习机（SADE-ELM）作为替代干旱预测工具的应用较少。本研究的目的是评估这些模型预测干旱的能力和干旱指标的定量价值，标准化降水指数（SPI）
Drought occurs throughout the world, affecting people more than any other major natural hazards – especially in the agriculture industry. An effective and timely monitoring system is required to mitigate the impacts of drought. Meanwhile, extreme learning machine (ELM), online sequential extreme learning machine (OS-ELM), and self-adaptive evolutionary extreme learning machine (SADE-ELM) are rarely applied as the alternative drought-forecasting tools in the meantime. The present study aims to evaluate the ability of these models to predict drought and the quantitative value of drought indices, the standardized precipitation index (SPI)