Browsing by Author "Haghighi, Ali Torabi"

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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 - WoS: 7
Citation - Scopus: 9
Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms
(PUBLIC LIBRARY SCIENCE, 2021) Enes Gul; Mir Jafar Sadegh Safari; Ali Torabi Haghighi; Ali Danandeh Mehr; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Haghighi, Ali Torabi; Gul, Enes
To reduce the problem of sedimentation in open channels calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems the development of machine learning based models may provide reliable results. Recently numerous studies have been conducted to model sediment transport in non-deposition condition however the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback six data sets with wide ranges of pipe size volumetric sediment concentration channel bed slope sediment size and flow depth are used for the model development in this study. Moreover two tree-based algorithms namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms M5RT and M5RGT provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.1 84 and RMSE = 1.071 respectively. In order to recommend a practical solution the tree structure algorithms are supplied to compute sediment transport in an open channel flow.