Details

Autor(en) / Beteiligte

• Danandeh Mehr, Ali

Titel

A Gene-Random Forest Model for Meteorological Drought Prediction

Ist Teil von

• Pure and applied geophysics, 2023-07, Vol.180 (7), p.2927-2937

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2023

Quelle

SpringerLink Journals

Beschreibungen/Notizen

• 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.