Drought Assessment Based on Data Fusion and Deep Learning

Abstract

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.

Hydrological Drought Forecasting Using a Deep Transformer Model

“…The research compared the transformer and LSTM models, with results showing the superiority of transformer models over LSTM-especially for long-term prediction. The outcomes of this research are consistent with the findings of other studies [28,38] that used DL models for hydrological drought prediction, showing similar quality in the prediction of drought events. For instance, Adikari et al [38] predicted high runoff values more accurately, since the LSTM models do not overestimate high flow values.…”

“…This was also true for this study, as the LSTM models predicted high-stage data better than the transformer models. Moreover, Li et al [28] achieved a high prediction accuracy when comparing the LSTM model to ARIMA, showing the superiority of the LSTM model over the ARIMA model. Although LSTM was proposed to tackle the impact of short-term memory for the better prediction of longer time sequences, the transformer models have an advantage in that they incorporate a seasonal trend decomposition scheme, which can significantly boost prediction outcome by about 50-80% [41].…”

“…However, data-driven models do not consider catchment processes, given that they are less data-intensive and act as black-box hydrological models, i.e., based on input-output relationships instead of physical mechanisms. While a plethora of literature has used data-driven approaches for meteorological drought forecasting (e.g., [22][23][24]), fewer studies have attempted data-driven models for hydrological drought forecasting (e.g., [5,7,9,11,12,20,[25][26][27]), while other studies (e.g., [6,[28][29][30][31][32][33][34][35][36]) have focused on drought hindcasting and evaluation of model prediction (Table 1). There is a clear distinction between forecasting and hindcasting.…”

“…Of these models, LSTM is unique because it has the memory of the last information for use in the following input; however, it requires more resources and time to train long data sequences [28]. The LSTM model-an advanced learning algorithm and architecture for deep learning-can extract features that can aid in understanding complex relationships for large time-series data.…”

Retracted: Drought Assessment Based on Data Fusion and Deep Learning

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”