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2 Acta Geophysica

2 2022

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Multitemporal meteorological drought forecasting using Bat-ELM

Gholizadeh Reza, Yılmaz Hasan, Danandeh Mehr Ali

Acta Geophysica

2022

Vol. 70, no 2

917--927

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.

Drought forecasting using new advanced ensemble-based models of reduced error pruning tree

Shahdad Mojtaba, Saber Behzad

Acta Geophysica

2022

Vol. 70, no 2

697--712

The present study investigates the prediction accuracy of standalone Reduced Error Pruning Tree model and its integration with Bagging (BA), Dagging (DA), Additive Regression (AR) and Random Committee (RC) for drought forecasting on time scales of 3, 6, 12, 48 months ahead using Standard Precipitation Index (SPI), which is among the most common criteria for testing drought prediction, at Kermanshah synoptic station in western Iran. To this end, monthly data obtained from a 31-year period record including rainfall, maximum and minimum temperatures, and maximum and minimum relative humidtty rates were considered as the required input to predict SPI. In addition, different inputs were combined and constructed to determine the most effective parameter. Finally, the obtained results were validated using visual and quantitative criteria. According to the results, the best input combination comprised both meteorological variable and SPI along with lag time. Although hybrid models enhanced the results of standalone models, the accuracy of the best performing models could vary on different SPI time scales. Overall, BA, DA and RC models were much more effective than AR models. Moreover, RMSE value increased from SPI (3) to SPI (48), indicating that performance modeling would become much more challenging and complex on higher time scales. Finally, the performance of the newly developed models was compared with that of conventional and most commonly used Support Vector Machine and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, regarded as the benchmark. The results revealed that all the newly developed models were characterized by higher prediction power than ANFIS and ANN.