Browsing by Author "Mehr, Ali Danandeh"

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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: 8
Citation - Scopus: 9
A novel stabilized artificial neural network model enhanced by variational mode decomposing
(CELL PRESS, 2024) Ali Danandeh Mehr; Sadra Shadkani; Laith Abualigah; Mir Jafar Sadegh Safari; Hazem Migdady; Mehr, Ali Danandeh; Migdady, Hazem; Shadkani, Sadra; Safari, Mir Jafar Sadegh; Abualigah, Laith; Danandeh Mehr, Ali
Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues we propose a stabilized ANNs called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta T & uuml,rkiye. To enhance SANN forecasting accuracy we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities respectively.
Citation - WoS: 22
Citation - Scopus: 23
An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels
(ELSEVIER, 2020) Zohreh Sheikh Khozani; Mir Jafar Sadegh Safari; Ali Danandeh Mehr; Wan Hanna Melini Wan Mohtar; Mehr, Ali Danandeh; Khozani, Zohreh Sheikh; Safari, Mir Jafar Sadegh; Wan Mohtar, Wan Hanna Melini; Sheikh Khozani, Zohreh; Mohtar, Wan Hanna Melini Wan; Danandeh Mehr, Ali
Assimilating unique features of genetic programming (GP) and gene expression programming (GEP) this study introduces a hybrid algorithm which results in promising incipient non-cohesive sediment motion models. The new models use the dimensionless input parameters including relative particle size relative deposited bed thickness channel friction factor and channel bed slope to estimate particle Froude number in rectangular channels. The models' accuracy is tested using different error measures and cross-validated through comparison with that of five empirical models available in the relevant literature. The results showed enhanced accuracy of the proposed models in comparison to the existing ones with concordance correlation coefficient of 0.92 and 0.94 for parsimonious and quasi-parsimonious ensemble GP models respectively. Such superiority is attributed to the integrated use of flow fluid sediment and channel characteristics in the modeling of incipient motion. Although the new algorithm is hybrid the proposed models are explicit and precise and thus motivating to be used in practice.
Citation - WoS: 18
Citation - Scopus: 21
Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes
(ASCE-AMER SOC CIVIL ENGINEERS, 2020) Ali Danandeh Mehr; Mir Jafar Sadegh Safari; Danandeh Mehr, Ali; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh
Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models this study investigates the capabilities of soft computing methods including multigene genetic programming (MGGP) gene expression programming and multilayer perceptron to derive accurate sewer design models. A wide range of experimental data sets comprising fluid flow sediment and pipe features was used to develop new models under the nondeposition with a deposited bed self-cleansing condition. The results showed better performances of the new models compared to the conventional ones in terms of statistical performance indices. The proposed MGGP model was found superior to its counterparts. It is an explicit model motivated to be used for self-cleansing sewer pipes design in practice.
Assessing Seasonal Drought Persistence Using a Bayesian Logistic Regression Approach
(Pergamon-Elsevier Science Ltd, 2026) Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ahmed, Abdelkader T.; Ali, Zulfiqar; Raza, Muhammad Ahmad; Danandeh Mehr, Ali; Niaz, Rizwan
This study investigates the patterns and intraseasonal predictability of meteorological drought (MD) through exploring the frequency and persistence of drought events. To this end, 52 years of precipitation measurements at six meteorology stations located in Ankara Province of Türkiye were used. Standardized Precipitation Index (SPI) at 3-month accumulation period, i.e., SPI-3, was calculated to represent local MD conditions. To evaluate the likelihood and odds of MD events a single variable Bayesian Logistic Regression approach was employed. Our findings showed that both frequency and intraseasonal persistence of MD events range from 40 % to 90 % in the region. Certain areas, such as Beypazari, Nallihan, and Kizilcahamam were found particularly vulnerable to drought and are more likely to experience drought persistence between successive seasons. Furthermore, the results revealed a negative correlation between spring drought occurrences and winter SPI-3 records, indicating a heightened exposure to drought persistence from winter to spring, while demonstrating reduced vulnerability during the transition from summer to fall. Providing a robust probabilistic framework for assessing drought persistence, this study contributes to improving drought risk management in the region.
Citation - WoS: 47
Citation - Scopus: 53
Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes
(IRTCES, 2020) Isa Ebtehaj; Hossein Bonakdari; Mir Jafar Sadegh Safari; Bahram Gharabaghi; Amir Hossein Zaji; Hossien Riahi Madavar; Zohreh Sheikh Khozani; Mohammad Sadegh Es-haghi; Aydin Shishegaran; Ali Danandeh Mehr; Bonakdari, Hossein; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Zaji, Amir Hossein; Gharabaghi, Bahram; Madavar, Hossien Riahi; Riahi Madavar, Hossien; Danandeh Mehr, Ali; Ebtehaj, Isa
Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence the limiting velocity should be determined to keep the channel bottom clean from sediment deposits. Recently sediment transport modeling using various artificial intelligence (AI) techniques has attracted the interest of many researchers. The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems. A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport. Utilizing one to seven dimensionless parameters 127 models are developed in the current study. In order to evaluate the different parameter combinations and select the training and testing data four strategies are considered. Considering the densimetric Froude number (Fr) as the dependent parameter a model with independent parameters of volumetric sediment concentration (C-V) and relative particle size (d/R) gave the best results with a mean absolute relative error (MARE) of 0.1 and a root means square error (RMSE) of 0.67. Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods. The percentage of the observed sample data bracketed by 95% predicted uncertainty bound (95PPU) is computed to assess the uncertainty of the best models. (C) 2019 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Design of smart urban drainage systems using evolutionary decision tree model
(Institution of Engineering and Technology, 2020) Mir Jafar Sadegh Safari; Ali Danandeh Mehr; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh
Recently as an alternative method for monitoring of drainage systems Internet of Things (IoT) technology is initiated in smart cities. IoT is used for detection of the location of the sediment deposition within the drainage pipe system to alert for repairing before complete blocking. However from the hydraulic point of view it is reasonable to design the drainage and sewer pipes to prevent the deposition of the sediment based on the physical parameters. To this end instead of detection of blockage location monitoring the flow characteristics is of more importance to keep pipe bottom clean from sediment deposition. Accordingly smart sensors mounted in the drainage and sewer pipes should read the flow velocity and alert once the flow reaches a velocity in which sediment deposition is occurred. In order to determine the sediment deposition velocity this study models sediment transport in drainage systems by means of evolutionary decision tree (EDT) technique. EDT results are compared with conventional decision tree (DT) and evolutionary genetic programming (GP) techniques. A large number of experimental data covering wide ranges of sediment and pipe size were used for the modeling. Evaluation of the developed models in terms of verity of statistical indices showed the outperformance of the proposed EDT model. The EDT DT and GP models were found superior to their traditional corresponding regression models existing in the literature. Results are helpful for determination of the flow characteristics at sediment deposition condition in drainage systems maintained using IoT technology in smart cities. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 61
Citation - Scopus: 64
Drought modeling using classic time series and hybrid wavelet-gene expression programming models
(ELSEVIER, 2020) Saeid Mehdizadeh; Farshad Ahmadi; Ali Danandeh Mehr; Mir Jafar Sadegh Safari; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ahmadi, Farshad; Danandeh Mehr, Ali; Mehdizadeh, Saeid
The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e. SPEI-3 SPEI-6 and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end two types of classic time series models namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore the hybrid models are proposed by coupling the wavelet (W) and gene expression programming (GEP). Five various mother wavelets (i.e. Haar db4 Symlet Meyer and Coifflet) for the first time are employed and compared for implementing the hybrid W-GEP approach in drought modeling. The modeling results of SPEI droughts via the time series models illustrated that the non-linear BL performs slightly better than the linear AR. Moreover all the hybrid W-GEP models developed in the study region provide superior performances compared to the standalone GEP. In general db4 in SPEI-3 modeling and Symlet for modeling the SPEI-6 and SPEI-12 of the studied locations are the optimal wavelets to develop the W-GEP. Finally the SPEI series at each target station is modeled through applying the SPEI data of the five neighboring stations. It is found that the SPEI data of neighboring stations are appropriate for modeling the SPEI series of the target station when the SPEI data of the target station is not at hand. For this case the performance of standalone GEP for modeling the SPEI-3 and SPEI-6 of the stations is generally better than the case of utilizing the original SPEI data at each target station.
Citation - WoS: 5
Citation - Scopus: 5
Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree
(GAZI UNIV, 2020) Ali Danandeh Mehr; Farzaneh Bagheri; Mir Jafar Sadegh Safari; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Danandeh Mehr, Ali; Bagheri, Farzaneh
Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper for the first time compares the efficiency of the decision tree (DT) and classic genetic programming (GP) data mining models developed for electrical energy demand forecasting in Nicosia Northern Cyprus. The models were trained and tested using daily electricity consumptions measured during the period 2011-2016 and were compared in terms of three statistical performance indices including coefficient of determination mean absolute percentage error and concordance coefficient. The prediction results showed that the proposed models can be effectively applied to forecasts of electrical energy demand. The results also indicated that the GP is slightly superior to DT in terms of the performance indices.
Citation - WoS: 2
Citation - Scopus: 1
Multi-level trend analysis of extreme climate indices by a novel hybrid method of fuzzy logic and innovative trend analysis
(Nature Research, 2025) Fereshteh Modaresi; Ali Danandeh Mehr; Iman Sardarian Bajgiran; Mir Jafar Sadegh Safari; Mehr, Ali Danandeh; Bajgiran, Iman Sardarian; Safari, Mir Jafar Sadegh; Danandeh Mehr, Ali; Modaresi, Fereshteh
Multi-level trend analysis of extreme climate variables is an efficient method for in-depth investigation of the climate change impacts on ecohydrology. However most of existing statistical methods do not reveal potential trends in different levels of data. In this study a new approach namely Fuzzy Innovative Trend Analysis (FITA) was introduced that takes the advantages of fuzzy logic to improve and facilitate Innovative Trend Analysis (ITA) abilities to multilevel trend detection at Extreme Climate Indices (ECIs). Regarding the graphical nature of the proposed method two new indices namely Grow Percent (GP) and Total Grow Percent (TGP) were suggested for quantifying the power of trend at distinct levels. The FITA was utilized for trend detection at three levels of four important ECIs related to precipitation and temperature. To this end long-term (1960–2021) daily temperature and precipitation observations at six meteorology stations across diverse climatic zones of Iran were used. The multilevel trends attained by the FITA were further compared to those of ITA Mann-Kendall (M-K) and Sen’s slope (SS) tests. The results indicated that the FITA provides promising results with higher interpretability and reliability than its counterparts at all stations. The underlying high-resolution trends detected at certain stations also pointed out that the M-K and SS tests may yield in misleading interpretations when they are used for identifying trends in ECIs. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 17
Citation - Scopus: 14
Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts
(Springer, 2020) Ali Danandeh Mehr; Mir Jafar Sadegh Safari; Danandeh Mehr, Ali; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh
It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues the present paper introduces a hybrid machine learning model namely multiple genetic programming (MGP) that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin Iran. The efficiency of the MGP was cross-validated against the benchmark models namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40% respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 40
Citation - Scopus: 44
Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm
(ELSEVIER, 2020) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Ali Danandeh Mehr; Rahimzadeh Arashloo, Shervin; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Danandeh Mehr, Ali
In this study Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear and consequently the common linear approaches fail to provide satisfactory performance. The calibration and verification processes of the RRKHS for one- and mull-day ahead forecasting R-R models were demonstrated using daily rainfall and streamflow measurement from a mountainous catchment located in the Black Sea region Turkey. The efficacy of the new approach in each forecasting scenario was compared with those of other benchmarks namely radial basis function artificial neural network and multivariate adaptive regression splines. The results illustrate the superiority of the RRKHS approach to its counterparts in terms of different performance indices. The range of relative peak error (PE) is found as 0.009-0.299 for the best scenario of the RRKHS model which illustrates the high accuracy of RRKHS in peak streamflow estimation. The superior performance of the RRKHS model may be attributed to its formulation in a very high (possibly infinite) dimensional space which facilitates a more accurate regression analysis. Based on the promising results of the current study it is expected that the proposed approach would be applied to other similar environmental modeling problems.
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: 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.
Citation - WoS: 21
Citation - Scopus: 22
VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Ali Danandeh Mehr; Masoud Reihanifar; Mohammed Mustafa Alee; Mahammad Amin Vazifehkhah Ghaffari; Mir Jafar Sadegh Safari; Babak Mohammadi; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ghaffari, Mahammad Amin Vazifehkhah; Vazifehkhah Ghaffari, Mahammad Amin; Reihanifar, Masoud; Mohammadi, Babak; Alee, Mohammad Mustafa; Danandeh Mehr, Ali
Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However various earth science and environmental databases are available that can be used for hydrological studies even at a catchment scale. In this study the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model called VMD-GP uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then the variational mode decomposition (VMD) in conjunction with state-of-the-art genetic programming is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks. © 2023 Elsevier B.V. All rights reserved.
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.