Browsing by Author "Mehr, Ali Danandeh"

Now showing 1 - 11 of 11

Citation - WoS: 8
Citation - Scopus: 9
A novel stabilized artificial neural network model enhanced by variational mode decomposing
(CELL PRESS, 2024-07) 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: 18
Citation - Scopus: 21
Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes
(ASCE-AMER SOC CIVIL ENGINEERS, 2020-05) 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, 2026-02) 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.
Assessing Seasonal Drought Persistence Using a Bayesian Logistic Regression Approach
(Pergamon-Elsevier Science Ltd, 2026-02) 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: 3
Citation - Scopus: 3
Design of smart urban drainage systems using evolutionary decision tree model
(Institution of Engineering and Technology, 2020-03-27) 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, 2020-08) 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, 2020-03-01) 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: 40
Citation - Scopus: 44
Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm
(ELSEVIER, 2020-08) 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: 7
Citation - Scopus: 9
Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms
(PUBLIC LIBRARY SCIENCE, 2021-10-08) 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), 2023-07-25) 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, 2021-07-01) 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.