Browsing by Author "Jabarnejad, Masood"

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Citation - WoS: 28
Citation - Scopus: 28
A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting
(MDPI, 2022) Ali Danandeh Mehr; Ali Torabi Haghighi; Masood Jabarnejad; Mir Jafar Sadegh Safari; Vahid Nourani; Mehr, Ali Danandeh; Jabarnejad, Masood; Haghighi, Ali Torabi; Safari, Mir Jafar Sadegh; Nourani, Vahid; Torabi Haghighi, Ali; Danandeh Mehr, Ali
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.
Citation - Scopus: 1
MOGGP: A novel multi objective geometric genetic programming model for drought forecasting
(Elsevier Ltd, 2025) Ali Danandeh Mehr; Masood Jabarnejad; Mir Jafar Sadegh Safari; Danandeh Mehr, Ali; Jabarnejad, Masood; Safari, Mir Jafar Sadegh
Drought is an environmental challenge with devastating impacts across a wide range of sectors including agriculture economy and ecosystems. Accurate drought forecasting models are necessary for sustainable water resources planning. Therefore exploring the efficacy and parsimony of emerging machine learning (ML) techniques to enhance predictive drought forecasting models’ accuracy while reducing their complexity is essential. This article introduces a novel hybrid evolutionary ML model called MOGGP and compares its efficiency with two evolutionary models namely gene expression programming and multigene genetic programming as well as conventional Multilayer Perceptron. The new model integrates multi-objective geometric mean optimizer with a traditional symbolic genetic programming that allows parsimonious model selection through developing Pareto optimal solutions. Grided Standardized Precipitation Evapotranspiration Index (SPEI) datasets were employed for demonstrating MOGGP and verifying its efficiency. The results showed that annual cycle is not an effective input for the evolved evolutionary SPEI model. In addition performance appraisal analysis revealed that the MOGGP consistently exhibits parsimonious models superior to its counterparts and excels in addressing multi-objective hydrological modeling problems. © 2025 Elsevier B.V. All rights reserved.