Danandeh Mehr, A., Torabi Haghighi, A., Jabarnejad, M., Safari, M. J. S., & Nourani, V. (2022). A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting. Water, 14(5), 755. https://doi.org/10.3390/w14050755
A new evolutionary hybrid random forest model for SPEI forecasting
|Author:||Danandeh Mehr, Ali1,2; Torabi Haghighi, Ali1; Jabarnejad, Masood3;|
1Water Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland
2Civil Engineering Department, Antalya Bilim University, Antalya 07070, Turkey
3Industrial Engineering Department, Dogus University, Istanbul 34775, Turkey
4Department of Civil Engineering, Yasar University, Izmir 35100, Turkey
5Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz 51666, Iran
6Faculty of Civil and Environmental Engineering, Near East University, Nicosia 99010, North Cyprus
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022052538690
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2022-06-29
State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
This research was supported by the Maa-ja vesitekniikan tuki r.y. (MVTT) with project number 41878.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).