School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Comput Intell Neurosci. 2022 Jul 31;2022:4429286. doi: 10.1155/2022/4429286. eCollection 2022.
Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to withstand drought risks. Taking the Yellow River Basin as the object, this paper utilizes data fusion, copula function, entropy theory, and deep learning, fuses the features of meteorological drought and hydrological drought into a drought assessment index, and establishes a long short-term memory (LSTM) network for drought assessment, based on deep learning theory. The results show that (1) after extracting the features of meteorological drought and hydrological drought, the drought convergence index (DCI) built on the fused features by copula function can accurately reflect the start and duration of the drought; (2) the drought assessment indices were effectively screened by judging the causality of the drought system, using the transfer entropy; (3) drawing on the idea of deep learning, LSTM for drought assessment, which was established on DCI and the drought assessment factors, can accurately assess the drought risks of the Yellow River Basin.
干旱是影响社会和经济可持续发展的主要因素。干旱评估研究对于制定干旱应急政策和干旱风险预警、提高抵御干旱风险的能力具有重要意义。本文以黄河流域为研究对象，利用数据融合、Copula 函数、熵理论和深度学习，将气象干旱和水文干旱的特征融合到一个干旱评估指标中，并基于深度学习理论建立了一个用于干旱评估的长短期记忆（LSTM）网络。结果表明：（1）在提取气象干旱和水文干旱特征后，Copula 函数构建的干旱收敛指数（DCI）能够准确反映干旱的开始和持续时间；（2）通过判断干旱系统的因果关系，利用传递熵有效地筛选了干旱评估指标；（3）借鉴深度学习的思想，基于 DCI 和干旱评估因子建立的 LSTM 可以准确评估黄河流域的干旱风险。
