Browsing by Author "Nourani, Vahid"

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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: 3
Citation - Scopus: 4
S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting
(SPRINGER HEIDELBERG, 2025) Ali Danandeh Mehr; Amir A. Ghavifekr; Elman Ghazaei; Mir Jafar Sadegh Safari; Chang-Qing Ke; Vahid Nourani; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ke, Chang-Qing; Nourani, Vahid; Ghazaei, Elman; Danandeh Mehr, Ali; Ghavifekr, Amir A.
Droughts are prolonged periods of rainfall deficit the frequency of which has increased due to global warming causing severe impacts on water resources agriculture ecosystems and food security. Given their significance accurate monitoring and forecasting of droughts are crucial for effective water resource management. This paper introduces sequential-based transformers (S-Transformer) a novel deep-learning approach aimed to apply for meteorological droughts prediction using their historical events. The core of the S-transformer algorithm is the orderly computing of an output by utilizing the sequence of inputs. Training of the S-transformer involves forward and backward passes through the network to adjust the weights and biases using gradient descent optimization. This process uses fixed-size dynamic windows to minimize the difference between the observed and forecasted outputs. To demonstrate the effectiveness and performance of the new model two case studies were presented based on the observed standardized precipitation index in Isparta and Burdur cities T & uuml,rkiye. In addition the S-Transformer efficiency was compared with those of three benchmark models including a classic multilayer perceptron a deep learning long-short-term memory and a deep classic transformer model. The promising results of the proposed model proved its superiority over its counterparts in terms of different performance metrics. In Isparta and Burdur cities the S-Transformer achieved the root mean squared values of 0.096 and 0.098 on the testing set respectively.
Citation - WoS: 22
Citation - Scopus: 27
Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting
(Turkish Chamber of Civil Engineers, 2021) Ali Danandeh Mehr; Mir Jafar Sadegh Safari; Vahid Nourani; Mehr, Ali Danandeh; Nourani, Vahid; Safari, Mir Jafar Sadegh; Danandeh Mehr, Ali
This study presents developing procedures and verification of a new hybrid model namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological stations at Ankara province Turkey. The new WPGP model comprises two main steps. In the first step the wavelet packet which is a generalization of the well-known wavelet transform is used to decompose the SPEI series into deterministic and stochastic sub-signals. Then classic genetic programming (GP) is applied to formulate the deterministic sub-signal considering its effective lags. To characterize the stochastic component different theoretical probability distribution functions were assessed and the best one was selected to integrate with the GP-evolved function. The efficiency of the new model was cross-validated with the first order autoregressive (AR1) GP and random forest (RF) models developed as the benchmarks in the present study. The results showed that the WPGP is a robust model superior to AR1 and RF and significantly increases the predictive accuracy of the standalone GP model. © 2023 Elsevier B.V. All rights reserved.