Multitemporal meteorological drought forecasting using Bat-ELM

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized precipitation evapotranspiration index (SPEI-3 and SPEI-6) is computed as the meteorological drought index at two study regions (Beypazari and Nallihan), located in Ankara province, Turkey. The proposed hybrid model is obtained by integrating the Bat optimization algorithm as the parameter optimizer with an extreme learning machine (ELM) as the regressor engine. The efficiency of the intended model was evaluated against the classic artificial neural network (ANN) and standalone ELM models. The evaluation and assessment are conducted using statistical metrics and graphical diagrams. The forecasting results showed that the accuracy of the proposed model outperformed the benchmark models. In a quantitative assessment, the Bat-ELM model attained minimal root mean square error for the SPEI-3 and SPEI-6 (RMSE = 0.58 and 0.43 at Beypazari station and RMSE = 0.53 and 0.37 at Nallihan station) over the testing phase. This indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

Automated summary

The advancement of machine learning models in geosciences has shown significant progress in predicting natural hazards. This article introduces a new hybrid model, Bat-ELM, which combines the Bat optimization algorithm and extreme learning machine for predictive drought modeling. The proposed model outperforms the classic artificial neural network and standalone ELM models in terms of forecasting accuracy, with a 20% improvement over traditional ANN and 15% improvement over classic ELM techniques.