Browsing by Author "Safari, Mir Jafar Sadegh"

Now showing 1 - 20 of 53

Citation - WoS: 9
Citation - Scopus: 11
A Joint Evaluation of Streamflow Drought and Standard Precipitation Indices in Aegean Region- Turkey
(SPRINGER BASEL AG, 2023-11-23) Ayse Gulmez; Denizhan Mersin; Babak Vaheddoost; Mir Jafar Sadegh Safari; Gokmen Tayfur; Tayfur, Gokmen; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Mersin, Denizhan; Gulmez, Ayse
Water is an invaluable substance that ensures the life cycle and causes hydrologic events worldwide. Water deficit also known as drought is a naturally occurring disaster that affects the hydrometeorologic and/or climatic responses in time and space. In this study the meteorologic and hydrologic droughts in Buyuk Menderes Kucuk Menderes and Gediz basins in Turkey are investigated. The streamflow drought index (SDI) and standard precipitation index (SPI) are used considering different time windows. To achieve this the monthly streamflow at Cicekli-Nif Besdegirmenler-Dandalas Bebekler-Rahmanlar and Kocarli-Koprubasi hydrometric stations together with monthly precipitation at 14 meteorologic stations during 1973-2020 (47 years) are used. The SDI and SPI with 1 3 6 and 12 months moving average are then used to express the association between the meteorologic and hydrologic droughts in the basin. Results showed that the SDI depicts no abnormal situations while the SPI rates in the 1980s and 2010s indicated severe droughts. It was concluded that the inner parts of the basins are prone to frequent droughts and there is a concordance between SPI and SDI patterns at the basin level. However minor discrepancies between SPI and SDI do exist and probably originated from temporal delays and water abstraction.
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: 2
Citation - Scopus: 3
A stochastic approach for the assessment of suspended sediment concentration at the Upper Rhone River basin- Switzerland
(SPRINGER HEIDELBERG, 2022-02-03) Babak Vaheddoost; Saeed Vazifehkhah; Mir Jafar Sadegh Safari; Vaheddoost, Babak; Vazifehkhah, Saeed; Safari, Mir Jafar Sadegh
This study addresses the link between suspended sediment concentration precipitation streamflow and direct runoff components. This is important since suspended sediment concentration in the streamflow has invaluable importance in the management of the river basin. For this the daily streamflow time series in five consecutive stations at Upper Rhone River Basin a relatively large basin in the Alpine region of Switzerland daily precipitation at one station and the twice a week suspended sediment concentration records at the most downstream station between January 1981 and October 2020 are used. Initially the base flow and the direct runoff associated with streamflow time series are obtained using the sliding interval method. Elasticity analyses between streamflow and suspended sediment concentration together with correlation autocorrelation partial autocorrelation stationarity and homogeneity are examined by the Augmented Dickey-Fuller and Pettitt's tests respectively. Then various stochastic scenarios are generated using the autoregressive moving average exogenous method (ARMAX). It is concluded that the precipitation and direct runoff have fewer effects on the suspended sediment concentration at downstream of the river. Hence the cumulative effect of the glacier or snowmelt and channel erosion may exceed the effect of rain blown washouts on the suspended sediment concentration at the Port du Scex station. It is found that the ARMAX model results are satisfactory and can be suggested for further application.
Citation - WoS: 12
Citation - Scopus: 16
Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region
(SPRINGER BASEL AG, 2021-05) Babak Vaheddoost; Mir Jafar Sadegh Safari; Vaheddoost, Babak; Safari, Mir Jafar Sadegh
This study addresses the application of signal processing in the evaluation of meteorological drought associated with monthly precipitation time series. Several drought indices and a Haar wavelet decomposition (WD) with ten components are implemented in the evaluation of the monthly precipitation of a mountainous region called Mount Uludag in Turkey. Monthly precipitation time series in three meteorological stations at the summit and foothills are used. The Standardized Precipitation Index (SPI) is used at monthly annual and 12- and 48-month moving average time frames as the benchmark to investigate the drought patterns. The results obtained by the WD and SPI are then confirmed using the Z-score index (ZSI) at monthly and annual scales together with the modified China Z-index (MCZI) and rainfall anomaly index (RAI) at a monthly scale. Changes in the moments of the distribution correlation analysis mutual information and power spectrum are applied to investigate the nature of the relationship between the sequences of precipitation events in time and space. The temporal correlation analysis together with the mutual information showed that the system has a short-term memory with strong seasonality. Similarly the power spectra depicted major seasonality at 1 3 5 6 12 22 and 60 months in the precipitation time series. It is concluded that the recent drought events have an infrequent nature which altered the sinusoidal patterns of the large-scale events. The SPI-48 and the WD showed that declines are strongly related to the large-scale cycles but the decline patterns are more related to the station located at the mountain summit.
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: 2
Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan
(Birkhauser, 2024-11-01) Gökmen Tayfur; Ehsanullah Hayat; Mir Jafar Sadegh Safari; Tayfur, Gokmen; Hayat, Ehsanullah; Safari, Mir Jafar Sadegh
Afghanistan is suffering from periodic events of drought which has exacerbated in recent years due to extreme climate events in the region. Having an arid to semi-arid climate the country faces significant challenges of water resources management especially for irrigation as reliance on agriculture is cumbersome. This study is undertaken to characterize historical meteorological drought in Afghanistan to provide an insight on where and when meteorological drought events happened in different River Basins (RBs). The study mainly employs the gamma-Standardized Precipitation Index (gamma-SPI) to analyze historical meteorological droughts across Afghanistan from 1979 to 2019. Monthly precipitation data is obtained from the Ministry of Energy and Water (MEW) of Afghanistan which is a combination of observed data from ground stations and gap-filled data by the MEW for the study period. Gridded gamma-SPI values are interpolated and mapped to visualize patterns of spatial drought across the entire country. The results indicate that countrywide extreme drought events occurred in 1999 2000 2001 2010 2016 2017 and 2019 particularly affecting southern western and southwestern regions. Decreasing rainfall occurred in all five RBs with the most considerable decline observed in the 1999–2008 period. The study reveals the increasing frequency and severity of meteorological droughts in Afghanistan. It also emphasizes on the vulnerability of agriculture and water sectors due to the drought events. The findings of the study suggest the need for better drought monitoring preparedness awareness and adaptation of strategies to ensure water security and agricultural sustainability in the face of climate change. © 2025 Elsevier B.V. All rights reserved.
Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan (Nov- 10.1007/s00024-024-03578-x- 2024)
(SPRINGER BASEL AG, 2025-01) Gokmen Tayfur; Ehsanullah Hayat; Mir Jafar Sadegh Safari; Tayfur, Gokmen; Hayat, Ehsanullah; Safari, Mir Jafar Sadegh
Assessment of Drought in Izmir District Using Standardized Precipitation Index
(Springer Nature, 2025) Tayfur, Gokmen; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Mersin, Denizhan; Gulmez, Ayse
Assessment of Drought in Izmir District Using Standardized Precipitation Index
(Springer Nature, 2025) Tayfur, Gokmen; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Mersin, Denizhan; Gulmez, Ayse
Closure to the discussion of Ebtehaj et al. on “Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach”
(Elsevier B.V., 2021-09) Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Jan Franklin Adamowski; Fathian, Farshad; Safari, Mir Jafar Sadegh; Mehdizadeh, Saeid; Adamowski, Jan
In this closure we respond to the comments of Ebtehaj et al. (2020) and also provide additional details regarding several features of our study. © 2021 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Comparability Analyses of Three Meteorological Drought Indices in Turkey
(CRC Press, 2023-07-24) Babak Vaheddoost; Mir Jafar Sadegh Safari; Vaheddoost, Babak; Safari, Mir Jafar Sadegh
The following chapter investigates the role of precipitation in the evaluation of meteorological drought in a mountainous region. For this Mount Uludag in Turkey was taken as the case of study. Three meteorological stations with quite long precipitation records were used. Monthly precipitation time series between January 1980 and October 2018 at the Keles and Osmangazi stations in the northern and southern hillsides together with the Uludag station near the summit were used in the analysis. Afterward the patterns in the data run frequency changes and temporal events related to the time series were evaluated using precipitation anomaly z-index autocorrelation mutual information and power spectrum. It was concluded that there is a strong seasonality in the data at every 6 and 12 months whereas the temporal persistence is quite low and decays after the second time lag. In the next stage three drought indices namely the Standardized Precipitation Index (SPI) Deciles Index (DI) and percent of normal (PN) were calculated at monthly seasonal and annual scales for each station. Finally a model based on the spatial temporal and spatiotemporal properties of the precipitation time series was developed using the multivariate adaptive regression splines (MARS) model. It was concluded that the spatial scenario is the best predictive model in the assessment of precipitation and drought and the SPI is the best one-parameter meteorological drought index for use in drought studies. © 2023 Elsevier B.V. All rights reserved.
Citation - Scopus: 62
Daily river flow simulation using ensemble disjoint aggregating M5-Prime model
(Elsevier Ltd, 2024-10) Khabat Khosravi; Nasrin Fathollahzadeh Attar; Sayed M. Bateni; Changhyun Jun; Dongkyun Kim; Mir Jafar Sadegh Safari; Salim Heddam; Aitazaz Ahsan Farooque; Soroush Abolfathi; Safari, Mir Jafar Sadegh; Abolfathi, Soroush; Jun, Changhyun; Bateni, Sayed M.; Kim, Dongkyun; Attar, Nasrin; Khosravi, Khabat
Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA) Disjoint Aggregating (DA) Additive Regression (AR) Vote (V) Iterative classifier optimizer (ICO) Random Subspace (RS) and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County US using a dataset comprising measured precipitation (Pt) evaporation (Et) and Qt. A wide range of input scenarios were explored for predicting Qt Qt+1 and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably relying solely on the most correlated variable (e.g. Qt-1) does not guarantee robust prediction of Qt. However extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s followed by ROF-M5P BA-M5P AR-M5P AR-M5P RS-M5P V-M5P ICO-M5P and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 % underscoring its efficacy and potential for advancing hydrological forecasting. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Data Reconstruction for Groundwater Wells Proximal to Lakes: A Quantitative Assessment for Hydrological Data Imputation
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03-01) Murat Can; Babak Vaheddoost; Mir Jafar Sadegh Safari; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Can, Murat
The reconstruction of missing groundwater level data is of great importance in hydrogeological and environmental studies. This study provides a comprehensive and sequential approach for the reconstruction of groundwater level data near Lake Uluabat in Bursa Turkey. This study addresses missing data reconstruction for both past and future events using the Gradient Boosting Regression (GBR) model. The reconstruction process is evaluated through model calibration metrics and changes in the statistical properties of the observed and reconstructed time series. To achieve this goal the groundwater time series from two observational wells and lake water levels during the January 2004 to September 2019 period are used. The lake water level the definition of the four seasons via the application of three dummy variables and time are used as inputs in the prediction of groundwater levels in observation wells. The optimal GBR model calibration is achieved by training the dataset selected based on data gaps in the time series while test-past and test-future datasets are used for model validation. Afterward the GBR models are used in reconstructing the missing data both in the pre- and post-training data sets and the performance of the models are evaluated via the Nash–Sutcliffe efficiency (NSE) Root Mean Square Percentage Error (RMSPE) and Performance Index (PI). The statistical properties of the time series including the probability distribution maxima minima quartiles (Q1–Q3) standard error (SE) coefficient of variation (CV) entropy (H) and error propagation are also measured. It was concluded that GBR provides a good base for missing data reconstruction (the best performance was as high as NSE: 0.99 RMSPE: 0.36 and PI: 1.002). In particular the standard error and the entropy of the system in one case respectively experienced a 53% and 35% rise which was found to be tolerable and negligible. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 43
Citation - Scopus: 48
Decision tree (DT) generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes
(IWA Publishing 12 Caxton Street London SW1H 0QS, 2019-03-15) Mir Jafar Sadegh Safari; Safari, Mir Jafar Sadegh
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates the bed load sediment transport in sewer pipes with particular reference to the non-deposition condition in clean bed channels. Four data sets available in the literature covering wide ranges of pipe size sediment size and sediment volumetric concentration have been utilized through applying decision tree (DT) generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) techniques for modeling. The developed models have been compared with conventional regression models available in the literature. The model performance indicators showed that DT GR and MARS models outperform conventional regression models. Result shows that GR and MARS models are comparable in terms of calculating particle Froude number and performing better than DT. It is concluded that conventional regression models generally overestimate particle Froude number for the non-deposition condition of sediment transport while DT GR and MARS outputs are close to their measured counterparts. © 2019 Elsevier B.V. All rights reserved.
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: 12
Citation - Scopus: 14
Discharge coefficient for vertical sluice gate under submerged condition using contraction and energy loss coefficients
(Elsevier Ltd, 2021-08) Babak Vaheddoost; Mir Jafar Sadegh Safari; Rasoul Ilkhanipour Zeynali; Zeynali, Rasoul Ilkhanipour; Vaheddoost, Babak; Ilkhanipour Zeynali, Rasoul; Safari, Mir Jafar Sadegh
A novel method is suggested for the determination of flow discharge in vertical sluice gates with considerably small bias. First in order to derive an equation for the discharge coefficient energy-momentum equations are implemented to define the physical realization of the phenomenon. Afterward the discharge coefficient is presented in terms of contraction and energy loss coefficients. Subsequently discharge coefficient contraction and energy loss coefficients were determined through an implicit optimization technique on the data. Data analysis illustrated that there is a meaningful power relationship between the contraction and energy loss coefficients. Thereafter dimensional analysis is performed and an explicit best-fit regression equation is developed for defining the energy loss coefficient. The obtained equations for contraction and energy loss coefficients were then used in the computation of the discharge coefficient and determination of the flow discharge in the vertical sluice gate. The performance of the developed approach is validated against the selected benchmarks existing in the literature. © 2021 Elsevier B.V. All rights reserved.
Drought Dynamics
(Elsevier, 2026) Vaheddoost, Babak; Safari, Mir Jafar Sadegh
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.