Browsing by Author "Safari, Mir Jafar Sadegh"

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Citation - WoS: 3
Citation - Scopus: 3
A CMIP6-based drought assessment over Küçük Menderes BasinTürkiye
(Springer, 2025) Farzad Rotbeei; Mustafa Nuri Balov; Mir Jafar Sadegh Safari; Babak Vaheddoost; Vaheddoost, Babak; Rotbeei, Farzad; Safari, Mir Jafar Sadegh; Nuri Balov, Mustafa
Droughts are the phenomenon of which their magnitude and frequency are forecasted to escalate over time primarily due to the impacts of climate change and global warming. Hence the potential consequences of the expected drought events are of the great importance in performing effective adaptation and regional mitigation strategies. The objective of the current study is to explore the consequences of climate change on the future droughts in Küçük Menderes Basin in western Türkiye. This objective will be addressed by examining the outputs of four General Circulation Models (GCMs) incorporated within Phase 6 of the Coupled Model Inter-comparison Project (CMIP6) with particular emphasis on two contrasting emission trajectories: SSP2-4.5 and SSP5-8.5. The daily precipitation and temperature projections are then utilized in determination of the so-called Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) drought indices with consideration to 2015–2039 as near future 2040–2069 as mid-term future and 2070–2099 as late future time frames. According to projections based on the SSP2-4.5 and SSP5-8.5 scenarios the number of dry months is anticipated to escalate by approximately 26.12% and 39.80% respectively toward the end of the twenty-first century (2070–2099) in contrast to the reference period (1985–2014). Results of the current study provide valuable insights for developing adaptation strategies to address future consequences of drought events in the Küçük Menderes Basin amid evolving climate conditions. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 10
A collaborative numerical simulation-soft computing approach for earth dams first impoundment modeling
(ELSEVIER SCI LTD, 2023) Behzad Shakouri; Mirali Mohammadi; Mir Jafar Sadegh Safari; Mohammad Amin Hariri-Ardebili; Hariri-Ardebili, Mohammad Amin; Shakouri, Behzad; Safari, Mir Jafar Sadegh; Mohammadi, Mirali
Uncertainty quantification plays a crucial role in the design monitoring and risk assessment of earth dams. To reduce the computational burden we employ a combination of finite difference method and soft computing techniques to investigate material uncertainties in earth dams during the initial impoundment stage. The findings of sensitivity analysis with the Tornado diagram indicate that key material properties such as dry density elasticity modulus friction angle and Poisson's ratio significantly influence the displacements and stress analysis. In our study we explore four variants of extreme learning machines (ELMs): the standalone ELM hybridized versions with the improved grey wolf optimizer algorithm ant colony optimization for continuous domains and artificial bee colony. These methods are assessed across various training sizes to predict multiple parameters including horizontal and vertical displacements stresses and the factor of safety (FoS). The hybridized ELM with the improved grey wolf optimizer algorithm emerges as the superior choice for most of the response variables. A minimum of 200 numerical simulations is required to establish a stable and accurate meta-model with an average prediction error of less than 3% for responses and the FoS.
Citation - WoS: 9
Citation - Scopus: 11
A Joint Evaluation of Streamflow Drought and Standard Precipitation Indices in Aegean Region- Turkey
(SPRINGER BASEL AG, 2023) 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: 1
Citation - Scopus: 1
A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models
(SPRINGER, 2025) Babak Mohammadi; Mirali Mohammadi; Babak Vaheddoost; Mustafa Utku Yilmaz; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Yilmaz, Mustafa Utku; Mohammadi, Babak
Accurate rainfall-runoff (RR) modeling holds significant importance in environmental management playing a central role in understanding the dynamics of water cycle. In this respect the precision in the determination of RR is crucial for mitigating the adverse effects of both water scarcity and excessive runoff ensuring the sustainable management of ecosystems and water resources. As a primary hydrological variable runoff engages in direct interactions with other hydrological variables. Due to the complexity of the RR process two primary approaches are commonly used in modeling namely conceptual (lumped) models and artificial intelligence (AI) models. Conceptual approaches are based on hydrological processes and use a larger number of hydrological variables yet they often exhibit lower performance compared to AI models. In contrast AI models rely on fewer parameters and lack physical interpretability but demonstrate high performance. This study merges the advantages of both lumped and AI techniques to develop an advanced RR model. Hence the applicability of several lumped and AI-based models in estimating the streamflow rates with the help of basic meteorological variables is investigated. The lumped hydrological models namely the Modello Idrologico SemiDistribuito in continuo (MISD) Identification of Unit Hydrographs and Component Flows from Rainfall Evaporation and Streamflow (IHACRES) and G & eacute,nie Rural & agrave, 4 param & egrave,tres Journalier (GR4J) are employed in conjunction with AI algorithms as Radial Basis Function (RBF) neural networks Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP). An ensemble of conceptual models (MISD IHACRES and GR4J) and three AI models (MLP RBF and ANFIS) with various lag times are considered as effective variables where Support Vector Machine (SVM) was utilized as a feature selection method with five different kernels in determining the best inputs. Afterward the SVM-ANFIS model as the best model is hybridized with Ant Colony Optimization (ACO) to develop the SVM-ANFIS-ACO model. It is found that the coupling of lumped and AI methodologies considerably enhanced the accuracy of the RR models, and SVM-ANFIS-ACO outperformed other models in streamflow computation.
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: 2
Citation - Scopus: 3
A stochastic approach for the assessment of suspended sediment concentration at the Upper Rhone River basin- Switzerland
(SPRINGER HEIDELBERG, 2022) 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: 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: 12
Citation - Scopus: 16
Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region
(SPRINGER BASEL AG, 2021) 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) 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: 3
Citation - Scopus: 2
Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan
(Birkhauser, 2025) 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) 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
Citation - WoS: 15
Citation - Scopus: 17
Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
(Elsevier Ltd, 2021) Khabat Khosravi; Mir Jafar Sadegh Safari; James R. Cooper; Khosravi, Khabat; Safari, Mir Jafar Sadegh; Cooper, James R.
Scour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for advanced machine learning (ML) algorithms to provide models of scour depth has yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid machine learning models. Using previously collected scour depth data from laboratory flume experiments the performance of five types of recently developed standalone machine learning techniques - the Isotonic Regression (ISOR) Sequential Minimal Optimization (SMO) Iterative Classifier Optimizer (ICO) Locally Weighted learning (LWL) and Least Median of Squares Regression (LMS) - are assessed along with their hybrid versions with Dagging (DA) and Random Subspace (RS) algorithms. The main findings are five-fold. First the DA-ICO model had the highest prediction power. Second the hybrid models had a higher prediction power than standalone models. Third all algorithms underestimated the maximum scour depth except DA-ICO which predicted scour depth almost perfectly. Fourth scour depth was most sensitive to densimetric particle Froude number followed by the non-dimensionalized contraction width flow depth within the contraction sediment geometric standard deviation approach flow velocity and median grain size. Fifth most of the algorithms performed best when all the input parameters were involved in the building of the model. An important exception was the best performing model that required only four input parameters: densimetric particle Froude number non-dimensionalized contraction width flow depth within the contraction and sediment geometric standard deviation. Overall the results revealed that hybrid machine learning algorithms provide more accurate predictions of scour depth than empirical equations and traditional ML-algorithms. In particular the DA-ICO model not only created the most accurate predictions but also used the fewest easily and readily measured input parameters. Thus this type of model could be of real benefit to practicing engineers required to estimate maximum scour depth when designing in-channel structures. © 2021 Elsevier B.V. All rights reserved.
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) 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 - 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 - Scopus: 1
Comparability Analyses of Three Meteorological Drought Indices in Turkey
(CRC Press, 2023) 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 - WoS: 52
Citation - Scopus: 56
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(Elsevier B.V., 2019) Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Jan Franklin Adamowski; Fathian, Farshad; Safari, Mir Jafar Sadegh; Mehdizadeh, Saeid; Adamowski, Jan F.
River flow rates are important for water resources projects. Given this the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to the same models hybridized with an autoregressive conditional heteroscedasticity (ARCH) model to estimate monthly streamflow. In addition two artificial intelligence (AI) approaches namely multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were explored. The performance of each of these models in estimating monthly streamflow was compared based on local and external data analyses. Using the local data analysis approach streamflow data at each target station was estimated using observed streamflow at the same station. The external data analysis approach used neighboring station streamflow data to estimate streamflow data for the target station. The Beinerahe Roodbar and Pole Astaneh stations on the Sefidrood River Iran as well as the Port Elgin and Walkerton stations on the Saugeen River Canada were used as study areas. Upstream and downstream monthly streamflow time series data were used. The performance of all models was compared using three error metrics including the root mean square error mean absolute error and correlation coefficient. The results showed that the hybrid time series models (i.e. AR-ARCH and MA-ARCH) outperformed the conventional AR and MA models. A comparison of all applied models revealed that the hybrid AR-ARCH and MA-ARCH time series models performed better than the AI techniques when using a local data analysis approach. The external data analysis approach was more accurate for monthly streamflow estimation than the local data analysis approach. To conclude based on the outcomes of the AI models under the external data analysis approach nearby data can be used to estimate streamflow of a target station when the target station streamflow data are not available. © 2019 Elsevier B.V. All rights reserved.
Citation - Scopus: 59
Daily river flow simulation using ensemble disjoint aggregating M5-Prime model
(Elsevier Ltd, 2024) 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.