Based on improved long short-term memory network feature attention extraction for china drought prediction

Abstract

Agricultural drought (AD) is a critical phenomenon that severely impedes crop productivity and global food security. While Deep Learning (DL) architectures have demonstrated significant potential in modeling soil moisture dynamics, existing methodologies frequently overlook inherent stochastic noise in multi-source datasets and the complex non-linear, often non-positive, correlations between hydroclimatological predictors and target variables. To address these challenges, this study proposes a novel Feature and Temporal Attention Extraction LSTM (FAELSTM) framework. The core of this architecture is a dual-attention (FAE) module designed to adaptively reweight hydroclimatological variables to mitigate data noise, coupled with a Bidirectional LSTM (BiLSTM) backbone to capture long-range spatiotemporal dependencies. Leveraging a long-term, high-resolution ERA5-Land dataset across China, we selected 11 hydroclimatological variables to serve as predictors for forecasting the Soil Moisture Condition Index (SMCI) over a specified future horizon. These variables include 2 m temperature (t2m), 10 m u/v-component of wind (u, v), precipitation (pre), surface pressure (ssr), specific humidity (spec), surface downward solar radiation (ssrd), surface downward thermal radiation (strd), soil temperature level 1 (stl1), total evaporation (e), and soil water capacity (swc). Experimental results demonstrate that FAELSTM consistently outperforms various state-of-the-art baselines. The model exhibits enhanced predictive precision and superior robustness, particularly in navigating class imbalance and data skewness to accurately identify severe and extreme drought stages where traditional architectures often falter. Furthermore, feature interpretability analysis confirms that the framework autonomously prioritizes key physical drivers—aligning with established hydrological water balance principles. This study validates the effectiveness of the FAELSTM framework in processing complex land-atmosphere coupled systems, providing a reliable decision-support tool for proactive agricultural water management and disaster mitigation.