Meteorological drought severity forecasting utilizing blended modelling

Abstract

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.

Keywords: Deep Learning Approach to Forecast Drought Severity; Drought prediction, Ensemble model; LSTM; RMSE; TabNet; XGBoost.

Graphical abstract

Fig. 1

Proposed system.

Fig. 2

Stacked LSTM model architecture.

Fig. 3

XGBoost model architecture.

Fig. 4

TabNet architecture.

Fig. 5

Taylor diagram.

Fig. 6

VOS-model performance network.