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A Gene-Random Forest Model for Meteorological Drought Prediction

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Abstract

The evolution of ensemble learning has recently offered a new approach to model complex systems. Inspired by the success of such methods, this paper introduces a new ensemble approach that integrates capabilities of two top state-of-the-art machine learning (ML) methods, namely random forests (RF) and genetic programming (GP), to model and forecast meteorological drought onset and severity. The new method, called gene-random forest (GeRF), follows the same steps of a standard GP systems, but with differences in the generation of initial population of potential solutions. The GeRF was tested to model and predict standardized precipitation evapotranspiration indices (SPEI-3 and SPEI-6) at two meteorology stations in Ankara province, Turkey. We have compared its efficiency to those of a classic autoregressive model as well as standalone RF, GP, and a hybrid ML model, called Bat-ELM, achieving results meaningfully superior to the benchmarks, particularly in the testing data.

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  1. Civil Engineering Department, Antalya Bilim University, Antalya, Turkey

    Ali Danandeh Mehr

  2. MEU Research Unit, Middle East University, Amman, Jordan

    Ali Danandeh Mehr

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  1. Ali Danandeh Mehr
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Correspondence to Ali Danandeh Mehr.

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Danandeh Mehr, A. A Gene-Random Forest Model for Meteorological Drought Prediction. Pure Appl. Geophys. 180, 2927–2937 (2023). https://doi.org/10.1007/s00024-023-03283-1

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