9 results on '"Saad Sh. Sammen"'
Search Results
2. A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)
- Author
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Zulfiqar Ali, Sadia Qamar, Nasrulla Khan, Muhammad Faisal, and Saad Sh. Sammen
- Subjects
Water Science and Technology ,Civil and Structural Engineering - Published
- 2023
3. Spatiotemporal variation of drought in Iraq for shared socioeconomic pathways
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Mohammed Magdy Hamed, Saad Sh. Sammen, Mohamed Salem Nashwan, and Shamsuddin Shahid
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Environmental Engineering ,Environmental Chemistry ,Safety, Risk, Reliability and Quality ,General Environmental Science ,Water Science and Technology - Published
- 2022
4. Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin
- Author
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Obaidullah Salehie, Tarmizi bin Ismail, Shamsuddin Shahid, Saad Sh Sammen, Anurag Malik, and Xiaojun Wang
- Subjects
Original Paper ,Trend analysis ,Environmental Engineering ,Group decision-making ,Gridded temperature data ,Environmental Chemistry ,Bioclimatic indicators ,Safety, Risk, Reliability and Quality ,Amu Darya ,Compromise programming ,Statistical metrics ,General Environmental Science ,Water Science and Technology - Abstract
Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision–making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann–Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03–0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest. Supplementary Information The online version contains supplementary material available at 10.1007/s00477-022-02172-8.
- Published
- 2022
5. A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization
- Author
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Lariyah Mohd Sidek, Mohammad Ehteram, Fatemeh Panahi, and Saad Sh. Sammen
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Index (economics) ,Health, Toxicology and Mutagenesis ,Particle swarm optimization ,General Medicine ,Pollution ,Multi-objective optimization ,Square (algebra) ,Gross domestic product ,Support vector machine ,Statistics ,Environmental Chemistry ,Energy (signal processing) ,Bat algorithm ,Mathematics - Abstract
The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.
- Published
- 2021
6. A new soft computing model for daily streamflow forecasting
- Author
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Ahmed El-Shafie, Mohammad Ehteram, R. A. Abdulkadir, Sani Isah Abba, Ali Najah Ahmed, and Saad Sh. Sammen
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Soft computing ,Environmental Engineering ,010504 meteorology & atmospheric sciences ,Mean squared error ,Computer science ,0208 environmental biotechnology ,Evolutionary algorithm ,Swarm behaviour ,Computational intelligence ,02 engineering and technology ,computer.software_genre ,Perceptron ,01 natural sciences ,020801 environmental engineering ,Streamflow ,Principal component analysis ,Environmental Chemistry ,Data mining ,Safety, Risk, Reliability and Quality ,computer ,0105 earth and related environmental sciences ,General Environmental Science ,Water Science and Technology - Abstract
Accurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient.
- Published
- 2021
7. Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal
- Author
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Saad Sh. Sammen, Romulus Costache, Quoc Bao Pham, Ismail Elkhrachy, Nguyen Thi Thuy Linh, Subha Chakraborty, Matej Vojtek, Satiprasad Sahoo, Jana Vojteková, and Ehsan Sharifi
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Hydrology ,Irrigation ,geography ,geography.geographical_feature_category ,Catchment land surface model ,010504 meteorology & atmospheric sciences ,Drainage basin ,Groundwater recharge ,GIS ,010502 geochemistry & geophysics ,01 natural sciences ,Terrestrial water storage ,Research Article - Hydrology ,Groundwater stress ,Infiltration (hydrology) ,Geophysics ,Groundwater storage ,Impervious surface ,Environmental science ,Soil moisture ,Water content ,Surface water ,Groundwater ,0105 earth and related environmental sciences - Abstract
Water is essential for irrigation, drinking and industrial purposes from global to the regional scale. The groundwater considered a significant water resource specifically in regions where the surface water is not sufficient. Therefore, the research problem is focused on district-wise sustainable groundwater management due to urbanization. The number of impervious surface areas like roofing on built-up areas, concrete and asphalt road surface were increased due to the level of urban development. Thus, these surface areas can inhibit infiltration and surface retention by the impact of urbanization because vegetation/forest areas are decreased. The present research examines the district-wise spatiotemporal groundwater storage (GWS) changes under terrestrial water storage using the global land data assimilation system-2 (GLDAS-2) catchment land surface model (CLSM) from 2000 to 2014 in West Bengal, India. The objective of the research is mainly focused on the delineation of groundwater stress zones (GWSZs) based on ten biophysical and hydrological factors according to the deficiency of groundwater storage using the analytic hierarchy process by the GIS platform. Additionally, the spatiotemporal soil moisture (surface soil moisture, root zone soil moisture, and profile soil moisture) changes for the identification of water stress areas using CLSM were studied. Finally, generated results were validated by the observed groundwater level and groundwater recharge data. The sensitivity analysis has been performed for GWSZs mapping due to the deficit of groundwater storage. Three correlation coefficient methods (Kendall, Pearson and Spearman) are applied for the interrelationship between the most significant parameters for the generation of GWSZ from sensitivity analysis. The results show that the northeastern (max: 1097.35 mm) and the southern (max: 993.22 mm) parts have high groundwater storage due to higher amount of soil moisture and forest cover compared to other parts of the state. The results also show that the maximum and minimum total annual groundwater recharge shown in Paschim Medinipore [(361,148.51 hectare-meter (ham)] and Howrah (31,510.46 ham) from 2012 to 2013. The generated outcome can create the best sustainable groundwater management practices based upon the human attitude toward risk. Electronic supplementary material The online version of this article (10.1007/s11600-020-00509-x) contains supplementary material, which is available to authorized users.
- Published
- 2021
8. An evaluation of existent methods for estimation of embankment dam breach parameters
- Author
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Saad Sh. Sammen, Lariyah Mohd Sidek, Abdul Halim Ghazali, Ahmed El-Shafie, and Thamer Ahmed Mohamed
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Estimation ,Atmospheric Science ,Engineering ,Hydrogeology ,Artificial neural network ,business.industry ,education ,0208 environmental biotechnology ,Regression analysis ,02 engineering and technology ,Civil engineering ,humanities ,020801 environmental engineering ,Dam failure ,Emergency response ,Earth and Planetary Sciences (miscellaneous) ,Embankment dam ,Geotechnical engineering ,business ,Uncertainty analysis ,Water Science and Technology - Abstract
The study of dam-break analysis is considered important to predict the peak discharge during dam failure. This is essential to assess economic, social and environmental impacts downstream and to prepare the emergency response plan. Dam breach parameters such as breach width, breach height and breach formation time are the key variables to estimate the peak discharge during dam break. This study presents the evaluation of existing methods for estimation of dam breach parameters. Since all of these methods adopt regression analysis, uncertainty analysis of these methods becomes necessary to assess their performance. Uncertainty was performed using the data of more than 140 case studies of past recorded failures of dams, collected from different sources in the literature. The accuracy of the existing methods was tested, and the values of mean absolute relative error were found to be ranging from 0.39 to 1.05 for dam breach width estimation and from 0.6 to 0.8 for dam failure time estimation. In this study, artificial neural network (ANN) was recommended as an alternate method for estimation of dam breach parameters. The ANN method is proposed due to its accurate prediction when it was applied to similar other cases in water resources.
- Published
- 2017
9. Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach
- Author
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Ahmed El-Shafie, Thamer Ahmed Mohamed, Saad Sh. Sammen, Azrul Ghazali, and Lariyah Mohd Sidek
- Subjects
Hydrology ,Engineering ,Mean squared error ,Artificial neural network ,business.industry ,Statistical index ,0208 environmental biotechnology ,02 engineering and technology ,Regression ,020801 environmental engineering ,Dam failure ,Approximation error ,Statistics ,Outflow ,business ,Smoothing ,Water Science and Technology ,Civil and Structural Engineering - Abstract
Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash – Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods.
- Published
- 2016
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