DSpace Arşivi :: by Yazar "Ghasemlounia, Redvan" değerine göre listeleniyor

Yazar "Ghasemlounia, Redvan" seçeneğine göre listele

Listeleniyor 1 - 20 / 33

A comparative study on novel hybrid approaches based on CEEMDAN, random forest, deep learning methods for predicting daily wind speed
(Springer Science and Business Media Deutschland GmbH, 2026) Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Mirabbasi, Rasoul; Haghighi, Ali Torabi
In this study, different kinds of hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithms with forecasting models including Random Forest (RF), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) neural networks, are developed to estimate the mean daily wind speed at the height of 2 m in A & gbreve;r & imath; city (WSst12), Turkey. In these hybrid models, different layer networks of single and integrated LSTM and GRU models include general single LSTM, general single GRU, simple coupled LSTM-GRU, and novel coupled LSTM with GRU through Addition layer (i.e., LSTM + GRU model) structures are applied. The most effective parameters on the WSst12, from a list of on-site potential meteorological parameters and wind speed values in its adjacent cities of A & gbreve;r & imath; province from Jan 2015-Dec 2019 through the Pearson correlation coefficient method, are determined. In the hybrid CEEMDAN and DNNs-based models, State activation functions (SAF), numbers of hidden neurons (NHN), dropout rates (P-rate), and network structural architect (NSA) as the meta-parameters are tuned for lessening the impact of overfitting/underfitting dilemmas and improving modeling performance. According to the comparison plots, performance evaluation measures, and total learnable parameter (TLP), the novel developed hybrid CEEMDAN-RF-(LSTM + GRU) model is confirmed as the best approach with an R2 of 0.86 while, in the optimal scenario using the RF model, R2 was 0.47.Graphical AbstractBased on the graphical snapshot, this study focuses on estimating daily mean wind speed at a 2-meter height in A & gbreve;r & imath;, Turkey, using hybrid data-driven models. The research integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with advanced forecasting techniques, including Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) neural networks. The modeling framework explores various configurations, such as standalone LSTM and GRU, coupled LSTM-GRU structures, and a novel LSTM + GRU model using an Addition layer to enhance predictive accuracy.
A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs
(IWA Publishing, 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan; Afaridegan, Ehsan; Haghiabi, AmirHamzeh; Mandala, Vishwanadham; Azamathulla, Hazi Mohammad; Parsaie, Abbas
In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%.
A new risk analysis method in the construction sector: HES
(Süleyman Demirel University, 2025) Koç, Nurettin; Ghasemlounia, Redvan
Hazardous environments are frequently present in the construction industry during both the project planning and implementation phases, with different risks emerging at each stage. Although various risk assessment methods are available to reduce workplace accidents, the industry prefers techniques that are adaptable to changing site conditions and easy to apply. This study examines the effectiveness of the Hazard Evaluation System (HES) matrix method compared to the traditional X-type and L-type matrix methods. The HES method provides a more detailed and comprehensive assessment by incorporating human factors such as employee training, age, severity, and probability. Since 88% of workplace accidents in the country are human-related, focusing on these elements allows for better risk mitigation. The study was carried out on a construction project in the Demre District of Antalya Province. Findings reveal that the HES method is more practical and reliable than traditional methods, while also improving project safety, enhancing efficiency, and generating economic advantages in construction operations.
A review o f criteria in rain water harvesting management
(Istanbul Gedik University, 2019) Noori, Shaho; Ghasemlounia, Redvan
Rainwater harvesting has gained renewed interest in the arid and semi-arid regions since the 1970s. It is important to consider how significant amounts of water can be harvest from a single catchment location. Papers selected address a wide range of rainwater harvesting problems, including the regionalization of nature curves. The rural population has limited income, a high susceptibility to climate change, conventional agricultural activities and adversely affected water shortages. Rain Water Harvest is an alternative cause to water shortages and groundwater depletion issues. The discovery of suitable rainwater harvesting sites is an essential move towards optimizing the amount of water harvesting and mitigating the ecological effect by using remote sensing and GIS techniques. In this article, the main requirements and parameters for selecting appropriate sites for rainwater harvesting have been extract from previous studies.
Advancement in forecasting rainfall-runoff process: application of a novel hybrid FMD-SVR-ABC modeling technique
(Springer International Publishing AG, 2026) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad
The prediction of monthly rainfall-runoff time series has a significant influence in planning and developing water resources projects. Thus, in this research, a novel advanced coupled predictive disintegration-optimization-based model is developed to improve the forecasting exactness of the mean monthly river's runoff. The suggested estimation model is a coupled version of the feature mode decomposition (FMD) algorithm and support vector regression (SVR) model optimized with artificial bee colony (ABC) metaheuristic algorithm, i.e., hybrid FMD-SVR-ABC model. Its performance is tested on monthly Barandouzchay River's runoff (BCRRm) watershed in Urmia City, West Azerbaijan Province from Sep 1971 to Aug 2022. In the FMD-based approaches, the optimal amount of mode number for the rainfall time series measured is 5. Using the partial autocorrelation function (PACF) technique, the number of predictor variables is determined as 9. Comparison plots and performance assessment criteria attest that the recommended model under the optimum predictor and meta-parameters tuned, provides better forecasting results with coefficient of determination (R2) of 0.82, root mean square error (RMSE) of 2.67 (m3/s), mean bias error (MBE) of 0.22 (m3/s), Nash-Sutcliffe efficiency (NSE) of 0.8. Comparatively, the individual SVR model leads to the R2 of 0.36, RMSE of 5.39 (m3/s), MBE of 2.23 (m3/s), and NSE of 0.23. Integrating with FMD and ABC algorithms lessens the RMSE value in the single SVR (as the benchmark model) by 27.8% and 15.7%, respectively. Therefore, the suggested hybrid model can be operated as an ingenious, sensible, and precise predictive model for the evaluation of the sequential rainfall-runoff rivers data, mainly the peak flows in different hydro-climatic regimes.
Analyzing traffic accident trends and correlations in Iraq: an investigative statistical approach
(American Institute of Physics, 2025) Hassooni, Dhuha Khalid; Ghasemlounia, Redvan; Hilal, Miami Mohammed; Al-Saffar, Zaid Hazim; Mohammed, Ghufran Taha
Traffic accidents are for two reasons, human-related and road-related structural behavior, two of which are in need of investigation. Herewith, this study embarks on an analysis of the dynamics of traffic accidents within Iraq, with a pronounced emphasis on statistical analyses concerning Baghdad, the capital city which is bearing the highest accident rates. An expansive dataset encompassing 10959 traffic incidents recorded over the year 2022 is utilized with this paper examining the distribution and nature of road accidents alongside the resultant degrees of injury. Through the deployment of Histograms and Q-Q Plots, the research confirms the normality of the data, paving the way for subsequent Pearson Correlation and ANOVA tests. These statistical methodologies reveal moderate, yet statistically insignificant, correlations between the nature of accidents and the characteristics of roads, with an F-statistic of 0.247 indicating no substantial effect of accident nature on the type of roads within Baghdad. Notably, the analysis extends to gender-referenced mortality records and root cause analyses that highlight significant seasonal fluctuations in accident occurrences, as well as pivotal gender disparities in road traffic incidents. The city-based accident records, detailed distributions of traffic accidents by nature, degree of injury, and recent accident trends, alongside statistical test visualizations, collectively underpin the analytical discourse. In conclusion, the paper asserts the critical necessity for targeted interventions and policy reforms aimed at mitigating these identified trends and contributing to the broader objective of enhancing road safety in Iraq.
Application of AI approaches to estimate discharge coefficient of novel kind of sharp-crested v-notch weirs
(American Society of Civil Engineers (ASCE), 2022) Gharehbaghi, Amin; Ghasemlounia, Redvan
In this study, the hydraulic features of the SCVW (a novel type of sharp-crested V-notch weirs) were scrutinized in the popular vertex angles theta, i.e., 30 degrees, 45 degrees, 60 degrees, 90 degrees, 120 degrees, 128 degrees, and 150 degrees, under aerated, steady and free overflow conditions in an open channel for large physical models. To assess the changes of the discharge coefficient of the SCVW (i.e., C-dSCVW) versus weir height and theta, widespread laboratory works were performed by measuring the water head over the crest of the weir and the discharge. Special formulas for the C-dSCVW in the theta=60 degrees were checked, and an appropriate empirical equation was recommended. The calculated C-dSCVW by the proposed equation was within 0%-10% of the measured values. Three types of nonparametric artificial intelligence (AI) methods, namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) were developed to estimate the C-dSCVW. For the sake of modeling, 196 experimental datasets were applied in the mentioned methods to evaluate the C-dSCVW by taking into consideration the dimensionless variables which impact the determining procedure of the C-dSCVW. In this modeling, by varying the architecture and core factors of the aforementioned methods, several scenarios were defined. The generated mathematical equation of C-dSCVW by the best scenario of the GEP was compared with the corresponding measured values in which the results were in 0%-10%. According to the attained statistical indices, scatter plots, and the values of the total grade (TG) technique, the hybrid SVR(RBF)-ACO model was determined as the superior and optimal method to estimate the C-dSCVW with high performance and accuracy.
Application of data-driven models to predict the dimensions of flow separation zone
(Springer Heidelberg, 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan; Latif, Sarmad Dashti; Haghiabi, Amir Hamzeh; Parsaie, Abbas
In this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes' shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55 degrees. In this direction, a butterfly's array for the vanes' arrangement along with different main controlling factors such as distances of vanes along the flow (delta(l)), degree of curvature (beta), and angles of attack to the local primary flow direction (theta) is utilized. Through capturing photos and utilizing AutoCAD and SURFER software, maximum relative length and width are calculated. Based on the experimental measurements, maximum percentage reduction of DFSZ, in comparison with the controlled test (without submerged vanes), is obtained with theta =30 degrees, beta = 34 degrees, and delta(l) = 10 cm with value of 78 and 76%, respectively. Moreover, several data-driven models, namely, gene expression programming (GEP), support vector regression (SVR), and a robust hybrid SVR with an ant colony optimization algorithm (ACO) (i.e., hybrid SVR-ACO model), are developed in order to predict DFSZ via the operative dimensionless variables realized by Spearman's rho and Pearson's coefficient processes. In accordance with the statistical metrics, model grading process, scatter plot, and the hybrid SVR(RBF)-ACO model are preferred as the best and most precise model to predict maximum relative length and width with a total grade (TG) of 6.75 and 5.8, respectively. The generated algebraic formula for DFSZ under the optimal scenario of GEP is equated with the corresponding measured ones and the results are within 0-10%.
Application of hybrid meta-heuristic-based data-driven models to forecast streamflow drought index
(Springer, 2026) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad
Drought is a complex natural catastrophe threatened markedly the societies. Thus, its precise prediction has a noteworthy effect in numerous sections such as water resources, nutrition, economics, industry, etc. In the current investigation, to evaluate the hydrological drought procedure in Nazlu River basin, Urmia City, West Azerbaijan province, the streamflow drought index (SDINRB) is applied. In this direction, the SDINRB in four different time measures including 3, 6, 9, and 12-month are calculated using 829 mean monthly streamflow datasets recorded from Aug 1951 to Aug 2020 by Tapik hydro-meteorological station. Then, two robust advanced hybrids support vector regression (SVR) with Harris hawk algorithm (HHO) and intelligent water drop (IWD) optimization algorithms i.e., hybrid SVR-HHO and SVR-IWD models, are developed to estimate the fluctuations pattern of SDINRB-3-12. Given this, to accomplish the optimum supportive models' structure, many scenarios are implemented by tuning meta-parameters such as a number of hidden neurons and deterministic factors of SVR, HHO, IWD algorithms. According to the performance assessment criteria and comparison plots, the hybrid SVR-RBF-HHO model under ideal meta-parameters is identified as the suitable model for predicting SDINRB-3-6 droughts, yet the hybrid SVR-RBF-IWD model is recognized as the appropriate model for forecasting SDINRB-9-12 droughts. Likewise, the best modelling performance is achieved by the hybrid SVR-RBF-HHO model in predicting SDINRB-6 drought. It results in an RMSE, R-2, NSE, and MBE of 0.23, 0.94, 0.92, and 0.072, respectively. Nonetheless, for the single SVR-RBF as the benchmark model is attained 0.43, 0.79, 0.78, and 0.045, respectively.
Assessment of water quality in Kirkuk city: using geographic information systems and multi-linear regression analysis
(MIM Research Group, 2026) Noori, Shaho Kh.; Naser, Ibrahim J.; Ghasemlounia, Redvan; Ibrahim, Mohammed O.; Raheem, Aram M.; Fayyadh, Imran A.
Climate change, pollution, and the degradation of water quality are making it harder to provide safe drinking water globally. For ensuring water safety, it is important to analyze its chemical and physical properties. Assessing the suitability of tap water for consumption according to World Health Organization (WHO) standards and Iraqi standards using GIS techniques and multi-linear regression analysis in Kirkuk City, Iraq, the study examines fourteen key water parameters across multiple city locations and conducted over 2020 and 2022, including pH, turbidity, conductivity, alkalinity, hardness, various ions, dissolved solids, temperature, and chlorine. GIS mapping was used to visualize the spatial distribution of these parameters, while multi-linear regression analysis was employed to identify statistical relationships between turbidity and other water quality indicators. The pH level of tap water in Kirkuk has been shown to be slightly to moderately alkaline, and turbidity exceeded acceptable limits in some locations and decreased slightly in 2022 from that in 2020. It was observed that total dissolved solids decreased during the period of this study and were under permissible standard limits. Electrical conductivity and total hardness remained stable and acceptable. The concentrations of most of the ions, including calcium, chloride, magnesium, sulfate, sodium, and chlorine, were lower than those of WHO and Iraqi standards. Potassium levels varied, meeting standards in some areas in 2020 but falling below WHO recommendations everywhere by 2022. The regression analysis revealed that turbidity was significantly influenced by total dissolved solids, electrical conductivity, and total hardness, with a strong correlation (R2 = 0.939), indicating that changes in water quality parameters are interdependent. However, the R2 value for single linear regression suggests potential variability, emphasizing the need for further data refinement and additional explanatory variables. The study highlights the need for ongoing monitoring and improvement of Kirkuk's water treatment processes, demonstrating the value of GIS-based spatial analysis combined with statistical modeling for identifying areas with compromised water quality and implementing targeted interventions to address these issues.
Closure to "application of ai approaches to estimate discharge coefficient of novel kind of sharp-crested v-notch weirs"
(American Society of Civil Engineers (ASCE), 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan
...
Design and analysis of a double composite truss girder for long span bridges using perfobond leiste
(Dr D. Pylarinos, 2025) Saleem, Jaafar J.; Ghasemlounia, Redvan; Muteb, Haitham H.
This research investigates the behavior and analyzes the response of a Double Composite Truss Girder (DCTG) under increasing static load, in comparison to a Single Composite Truss Girder (SCTG) under the same conditions. The benefits of a composite structure are demonstrated, namely its ability to produce a lightweight girder suitable for bridge superstructures, especially in long continuous spans with the idea of converting the concrete web to steel truss. The present study also aims to increase the girder’s ability to resist high hogging moments in the negative moment regions. The experimental work involved the fabrication and testing of eight composite truss girder specimens with constant dimensions of 2620 mm x 350 mm x 400 mm of steel warren truss girder under two-point static load. A validation study was conducted using numerical analysis methods along with the Abaqus software program to simulate the eight models. Perfobond Leiste (PRL) shear connectors were deployed and the slip ratio, deflection ratio, girder strength, and stress–strain relationship results for each sample were presented.
Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network
(Elsevier Science Ltd, 2021) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Saadatnejadgharahassanlou, Hamid
In this study, mean monthly groundwater level (GWL) fluctuations of four different monitoring piezometers are forecasted in an agricultural area by developing new proposed structures of BiLSTM based neural network models. For developing models, 186 monthly measured water table depth during time period (Sep 2001-Feb 2017) are employed. Due to a lack of meteorological variables records, the module of sequence-to-one is employed. By tuning several hyperparameters such as the number of hidden units (NHU), kind of state activation function (SAF), learning dropout rate (P-rate), and network topology pattern (NTP), the performance of designed models is improved. Based on results of modelling, in all designed models, the optimal P-rate is obtained 0.5, and the running time decrease by increasing P-rate in the same model. Since harsh fluctuations of GWL, the general Single-LSTM mode (1) and Single-BiLSTM model (2) perform too weak compared to other models. Nonetheless, by deepening models through adding the suitable hidden layers instead of many numbers of nodes, the performance of Single-BiLSTM model is improved strikingly. Based on statistical evaluation metrics, violin plot, and also TLP (Total Learnable Parameters) value, novel proposed model (5) (simple Double-BiLSTM model combined by a Multiplication layer (x)) is selected as the superior model. In the piezometer 4 with a range of 4.49 (m), the model (5) yielded in an R-2 of 0.89 and an RMSE of 0.17 (m), while in the same physical characteristics, the simple Double-BiLSTM model (3) yielded in an R-2 of 0.77 and an RMSE of 0.25 (m).
Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature
(Springer Science and Business Media B.V., 2024) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Albaji, Mohammad
The precise predicting of air temperature has a significant influence in many sectors such as agriculture, industry, modeling environmental processes. In this work, to predict the mean daily time series air temperature in Mu & gbreve;la city (AT(m)), Turkey, initially, five different layer structures of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning-based neural network models through the seq2seq regression forecast module are developed. Then, based on performance evaluation metrics, an optimal DL-based layer network structure designed is chosen to hybridize with the wavelet transform (WT) algorithm (i.e., WT-DNN model) to enhance the estimation capability. In this direction, among potential meteorological variables considered, the average daily sunshine duration (SSD) (hours), total global solar radiation (TGSR) (kw. hour/m(2)), and total global insolation intensity (TGSI) (watt/m(2)) from Jan 2014 to Dec 2019 are picked as the most effective input variables through correlation analysis to predict AT(m). To thwart overfitting and underfitting problems, different algorithm tuning along with trial-and-error procedures through diverse types of hyper-parameters are performed. Consistent with the performance evaluation standards, comparison plots, and Total Learnable Parameters (TLP) value, the state-of-the-art and unique proposed hybrid WT-(LSTM x GRU) model (i.e., hybrid WT with the coupled version of LSTM and GRU models via Multiplication layer (x)) is confirmed as the best model developed. This hybrid model under the ideal hyper-parameters resulted in an R-2 = 0.94, an RMSE = 0.56 (degrees C), an MBE = -0.5 (degrees C), AICc = -382.01, and a running time of 376 (s) in 2000 iterations. Nonetheless, the standard single LSTM layer network model as benchmark model resulted in an R-2 = 0.63, an RMSE = 4.69 (degrees C), an MBE = -0.89 (degrees C), AICc = 1021.8, and a running time of 186 (s) in 2000 iterations.
Developing a novel hybrid model based on GRU deep neural network and whale optimization algorithm for precise forecasting of river's streamflow
(Nature Research, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Mirabbassi, Rasoul; Torabi Haghighi, Ali
Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on the hydrological cycle. In this study, a novel innovative deep neural network (DNN) structure by integrating a double Gated Recurrent Units (GRU) neural network model with a multiplication layer and meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×–WOA model) is developed to improve the prediction accuracy and performance of mean monthly Chehel-Chai River’s streamflow (CCRSFm) in Iran. The Pearson’s correlation coefficient (PCC) and Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine the only precipitation (Pm) as the most effective input variable among a list of on-site potential climate time series parameters recorded in the study area. Thanks to a well-proportioned layer network structural framework in the suggested hybrid 2GRU×–WOA model, it leads to an appropriate total learnable parameter (TLP) compared to standard individual GRU and Bi-GRU as the benchmark models developed in the comparable meta-parameters. This hybrid model under the optimal meant meta-parameters tuned i.e., coupling a state activation functions (SAF) of tanh-softsign, dropout rate (P-rate) of 0.5, numbers of hidden neurons (NHN) of 70, outperforms with an R2 of 0.79, NSE of 0.76, MAE of 0.21 (m3/s), MBE of -0.11(m3/s), and RMSE of 0.36 (m3/s). Hybridizing the 2GRU× model with WOA algorithm causes to increase in the value of R2 by 6.8% and reduce in the value of RMSE by 20.4%. Comparatively, standard individual GRU and Bi-GRU models result in an R2 of 0.59 and 0.66, NSE of 0.55 and 0.6, MAE of 0.91 and 0.53 (m3/s), MBE of 0.047 and − 0.06 (m3/s), RMSE of 1.29 and 0.83 (m3/s), respectively.
Developing a novel hybrid model based on GRU deep neural network and whale optimization algorithm for precise forecasting of river's streamflow (vol 15, 19436, 2025)
(Nature Portfolio, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Mirabbasi, Rasoul; Torabi Haghighi, Ali
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.
Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow
(Springer Science and Business Media Deutschland GmbH, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Daneshvar, Shahaboddin; Ahmadi, Farshad
In this research, novel innovative DDN layer network structures by hybridizing double-LSTM model with an addition layer (+) (i.e., 2LSTM and 2LSTM + layer network models) are developed purposefully to enhance prediction performance of the mean monthly Maroon River streamflow (MRSFm) in Iran from October 1987 to September 2017. For doing so, to select the most effective parameters on MRSFm, the Pearson’s correlation coefficient (PCC) and Cosine amplitude sensitivity (CAS) as features selection process are carried out for potential meteorological variables in the study area (i.e., average monthly temperature (Tm), evaporation (ETm), and precipitation (Pm)) and target (MRSFm). The results show that Tm and ETm have an insignificant influence on MRSFm, thus, only Pm is used as the most effective input variable in predicting MRSFm. Due to a well-balanced network model’s structural outline in the suggested novel hybrid 2LSTM + model, it accordingly yields to a suitable total learnable parameter (TLP) compared to ordinary standalone LSTM and GRU as the benchmark models developed in the similar meta-parameters. This model under the optimal meant meta-parameters tuned i.e., state activation functions (SAF) = tanh-softsign, numbers of hidden neurons (NHN) = 75, dropout rate (P-rate) = 0.5, performs best among the models with an R2 of 0.68, NSE of 0.63, PBIAS of 41%, KGE of 0.79, and RMSE of 19.24 m3/s. Comparatively, a standard gated recurrent units (GRU) and LSTM as benchmark models using the optimal scenario generate the following results: R2 are 0.57 and 0.67, NSE are 0.53 and 0.61, PBIAS are 109 and 49%, KGE are 0.63 and 0.79, and RMSE are 21.11 and 19.32 m3/s, respectively. Generally, in all models, in the equal NHN, rising P-rate value reduces convergence time.
Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region
(Springer Science and Business Media Deutschland GmbH, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Vaheddoost, Babak; Ahmadi, Farshad
Drought is an intricate natural disaster that substantially menace the world. Its exact forecasting has a remarkable impact in several parts such as food production, business, industry, etc. In this study, in order to assess the drought procedure in Mahabad River basin, the temporal meteorological reconnaissance drought index (RDIMRB) in four diverse time scales including 3, 6, 9, and 12-month are computed using 576 monthly climatic datasets recorded from Sep 1974 to Aug 2022. To predict the time series RDIMRB, different standalone deep neural network (DNN) models including LSTM, GRU, Bi-directional LSTM (Bi-LSTM), and Bi-directional GRU (Bi-GRU) with the sequence-to-one regression module of forecasting (seq2one) are developed. For sake of this aim, the first 70% of data (395 months) and the last 30% of data (169 months) chronologically are used in the calibration and validation parts, respectively, to feed in the models development process. So as to achieve the most advantageous models’ structure, a lot of scenarios are adopted by tuning the meant meta-parameters including NHU (number of hidden units), SAF (state activation function), and P-rate (learning dropout rate). According to the performance assessment criteria, total learnable parameters (TLP) criterion, and comparison plots, the Bi-GRU model is verified as the most satisfactory model, and best results are obtained in RDIMRB-12. It under the epitome meant meta-parameters achieved (i.e., NHU = 120, P-rate = 0.5, and softsign as the suitable SAF) results in the RMSE, MBE, NSE, PBIAS, and R2 of 0.17, 0.011, 0.92, -2.02%, and 0.86, respectively, nonetheless for the GRU model are gotten 0.64, 0.071, 0.23, 17.97%, and 0.65, respectively.
Emergency action plan for Haditha dam failure scenario, Al-Anbar, Iraq
(Future Publishing LLC, 2025) Hameed, Yasameen; Ghasemlounia, Redvan; Mohammed, Thamer Ahmed; Al-Ansi, Abdulwahab
Dams are essential structures that regulate and manage water for human activities such as irrigation, power generation, flood control, and water supply. However, building and operating dams involve inherent risks that can lead to catastrophic consequences in case of failure, such failures can threaten the environment and populations downstream. Haditha Dam, Al-Anbar Governate, Iraq has been chosen as a case study due to its unique geological conditions (existence of limestone formations prone to karstification) and susceptibility to terrorist attacks. In this research, the risk factor for Haditha Dam is categorized as extremely high risk, with a Total Risk Factor (TRF) of 36. An emergency action plan that includes three possible failure scenarios has been proposed. Based on the flood maps, there is an urgent need for evacuation planning and the designation of safe and unsafe zones in the cities downstream of Haditha Dam to mitigate the consequences of a potential failure of the Dam. This plan aims to address immediate flood inundation, minimize loss of life, and manage the damage that could occur to infrastructure. As part of the emergency response strategy, an evacuation program has been proposed to protect lives and reduce the impact on affected populations.
Evaluation of drought using meteorological drought indices, a case study: Alanya (Türkiye)
(Artvin Çoruh University Natural Hazards Application and Research Center, 2024) Ghasemlounia, Redvan; Utlu, Mustafa
Drought is one of the most important challenges that many countries, especially countries in the Middle East region, are struggling with. Based on this, the study and monitoring of hydrological and drought factors is an important issue that can have a significant impact on management decisions in the field of water resources, especially in crisis management. Therefore, investigating the drought parameters is very important to understand the drought situation of a region. In this study, Alanya region, which is located on the southern coast of Turkey, was selected as a case study for drought analysis. Four drought indices for the selected region including: China Z-Index (CZI), Standardized Precipitation Index (SPI), Modified China Z-Index (MCZI) and Z-Score Index (ZSI) have been investigated. All these indicators have been investigated and evaluated using time scales of 1, 6, 12 and 24 months, the coefficient of determination (R2) has been calculated for each drought index with a different time scale and their results have been compared. The findings of the research showed that SPI and CZI drought indices performed better than other selected drought indices in identifying and effectively tracking drought severity. In addition to the study of dry events, wet events were also investigated, which indicates the presence of consecutive floods in the last years of the studied period in the region. The results indicated similar very dry events for the selected indicators in the 6-month period. Also, the rainfall trend for the period of 2015-2022 was taken into consideration to examine the rainfall of the last eight years. The results show that precipitation has decreased in recent years and has a downward trend in most months of the period in question, and the possibility of flood events due to sudden showers in the region has increased due to the continuation of droughts experienced in the years before 2015. Investigating soil moisture and vegetation for the selected period in the study area is also important for the evaluation of the drought level. Evaluation of the available land (vegetation) cover maps of the years 1975, 1985, 2000, 2010, 2020 and 2022 show that the vegetation cover has weakened over the years, and it has been evaluated as an indicator that the danger of drought in the region has increased.

Yazar "Ghasemlounia, Redvan" seçeneğine göre listele

Sayfa Başına Sonuç

Sıralama seçenekleri