Ahire Aaditya, Zade Nilima, Mujawar Umeed, Mehta Dimple, Kotecha Ketan
Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India.
Symbiosis Center for Applied Artificial Intelligence, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India.
MethodsX. 2025 Jun 20;15:103456. doi: 10.1016/j.mex.2025.103456. eCollection 2025 Dec.
Prediction of droughts has recently become imperative as frequency and intensity are increasing mostly due to climatic variation. Indeed, drought is a highly significant disaster that results in widespread damage to all kinds of ecosystems, agricultural production systems, and water resources systems. Accurate techniques of forecasting are necessary for the purpose. Conventional methods lack the intricate time-space correlation in meteorological data. The research proposes an ensemble of Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Tabular Network (TabNet) for a higher accuracy in drought forecasting. With the large meteorological dataset that involves temperature, precipitation, humidity, and wind speed as features, the model integrates:•The tree capabilities of XGBoost perform feature selection very effectively.•Temporal Pattern Analysis using LSTM.•Insight obtained from the attention mechanism-based TabNet.Empirical results demonstrate that the proposed ensemble outperforms individual models, achieving the lowest Root Mean Square Error (RMSE: 0.6582) and Mean Absolute Error (MAE: 0.5377), and the highest Coefficient of Determination (R²: 0.5069). Furthermore, it yields the best Nash-Sutcliffe Efficiency (NSE: 0.5107) and Kling-Gupta Efficiency (KGE: 0.6039), confirming its superiority in drought severity forecasting. The ensemble outperforms traditional models, aiding early drought warnings and water conservation planning.
随着干旱发生的频率和强度因气候变化而不断增加，干旱预测最近变得势在必行。事实上，干旱是一种极具影响力的灾害，会对各类生态系统、农业生产系统和水资源系统造成广泛破坏。为此，需要准确的预测技术。传统方法缺乏气象数据中复杂的时空相关性。本研究提出了一种由极端梯度提升（XGBoost）、长短期记忆网络（LSTM）和表格网络（TabNet）组成的集成模型，以提高干旱预测的准确性。该模型利用包含温度、降水、湿度和风速等特征的大型气象数据集，集成了以下几点：
•XGBoost的树模型能力能够非常有效地进行特征选择。
•使用LSTM进行时间模式分析。
•从基于注意力机制的TabNet中获得的洞察。
实证结果表明，所提出的集成模型优于单个模型，实现了最低的均方根误差（RMSE：0.6582）和平均绝对误差（MAE：0.5377），以及最高的决定系数（R²：0.5069）。此外，它还产生了最佳的纳什-萨特克利夫效率（NSE：0.5107）和克林-古普塔效率（KGE：0.6039），证实了其在干旱严重程度预测方面的优越性。该集成模型优于传统模型，有助于早期干旱预警和水资源保护规划。
