A5084071339- Groundwater and Isotope Geochemistry
- Groundwater flow and contamination studies
- Groundwater and Watershed Analysis
- Hydrological Forecasting Using AI
- Hydrology and Watershed Management Studies
- Flood Risk Assessment and Management
- Water resources management and optimization
- Geophysics and Gravity Measurements
- Water Quality Monitoring Technologies
- Hydrology and Drought Analysis
- Reservoir Engineering and Simulation Methods
- Water Quality and Pollution Assessment
- Energy Load and Power Forecasting
- Geochemistry and Geologic Mapping
- Water Treatment and Disinfection
- Urban Stormwater Management Solutions
- Climate variability and models
- Air Quality Monitoring and Forecasting
- Geophysical and Geoelectrical Methods
- Water Quality Monitoring and Analysis
- Transboundary Water Resource Management
- Coastal and Marine Dynamics
- Ocean Waves and Remote Sensing
- Air Quality and Health Impacts
- Wastewater Treatment and Reuse
United Arab Emirates University
2019-2025
Assiut University
2012-2022
South Valley University
2020
Minia University
2019
This study proposes two techniques: Deep Learning (DL) and Ensemble (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 was used as inputs the fifth well scenario-2 (S2): time series with lag up 20 days all wells. The results S1 prove that ensemble EDL generally performs superior DL estimation of each station using data remaining four except Paya Indah Wetland which method provide better estimates compared EDL....
Several investigations have recently considered the possible impacts of climate change and seawater level rise on intrusion in coastal aquifers. All revealed severity problem significance landward movement dispersion zone under condition rise. Most studies did not consider effects inland shoreline associate changes boundary conditions at seaside domain geometry. Such become more evident flat, low land, alluvial plans where large areas might be submerged with a relatively small increase...
Abstract Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of (T) relative (Rh). Daily data over 24 years Kula Terengganu station were obtained from the Malaysia...
High level of tropospheric ozone concentration, exceeding allowable has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric concentration major cities located Kuala Lumpur and Selangor, The proposed models were developed using three-year historical data for different parameters as input 24-hour 12-hour concentration. Different have investigated, viz. Linear Regression, Neural Network Boosted Decision Tree....
Abstract Rivers carry suspended sediments along with their flow. These deposit at different places depending on the discharge and course of river. However, deposition these impacts environmental health, agricultural activities, portable water sources. Deposition reduces flow area, thus affecting movement aquatic lives ultimately leading to change river course. Thus, data variation is crucial information for various authorities. Various authorities require forecasted in operate hydraulic...
Abstract In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a highly essential several applications in the field of water resources engineering. One main contributors modeling reliability optimization input variables to achieve model. The step selection proper combinations. Hence, developing algorithm that can determine optimal combinations crucial. This study introduces Genetic (GA) better combination...
Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development required determine the best machine learning (ML) algorithm for PV solar output forecasting. Existing studies have established superiority of artificial neural network (ANN) random forest (RF) algorithms field. However, more recent demonstrated promising forecasting performances by decision tree (DT), extreme gradient boosting...
A reliable model to predict the changes in water levels a river is crucial for better planning mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed river's level, on daily basis based collected data from 1990 2019 which used train and test proposed models. Different input combinations explored improve accuracy of model. Statistical indicators calculated examine reliability models other The comparison several data-driven...
The intensive agricultural expansion and rapid urban development in Abu Dhabi Emirate, United Arab Emirates (UAE) have resulted a major decline local regional groundwater levels. By using the latest release (RL06) of Gravity Recovery Climate Experiment (GRACE) satellite measurements Global Land Data Assimilation System (GLDAS) products, storage change was computed compared with time series in-situ monitoring wells over period 2010–2016. RL06 GRACE products from Jet Propulsion Laboratory...
Abstract Human health is at risk from drinking water contamination, which causes a number of problems in many parts the world. The geochemistry groundwater, its quality, origins groundwater pollution, and associated risks have all been subject substantial research recent decades. In this study, west Rosetta Nile branch Delta Aquifer examined for potential. Numerous quality indices were applied, such as index (WQI), synthetic pollution (SPI) models, assessment (HRA) method. limits measured...
Abstract: The generation and processes of wadi flash floods are very complex not well understood. In this paper, we investigate the relationship between variations in geomorphometric rainfall characteristics responses floods. An integrated approach was developed based on analysis hydrological modeling. Wadi Qena, which is located Eastern Desert Egypt, selected to validate divided into 14 sub-basins with areas ranging from 315 1488 km2. distributed Hydrological River Basin Environment...
Water Quality Index (WQI) is the most common determinant of quality stream-flow. According to Department Environment (DOE, Malaysia), WQI chiefly affected by six factors, which are, chemical oxygen demand (COD), biochemical (BOD), dissolved (DO), suspended solids (SS), -potential for hydrogen (pH), and ammoniacal nitrogen (AN). In fact, understanding inter-relationships between these variables can improve predicting better water resources management. The aim this study create an input...
(2021). Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine algorithms. Engineering Applications of Computational Fluid Mechanics: Vol. 15, No. 1, pp. 902-933.
The suspended sediment load (SSL) is one of the major hydrological processes affecting sustainability river planning and management. Moreover, sediments have a significant impact on dam operation reservoir capacity. To this end, reliable applicable models are required to compute classify SSL in rivers. application machine learning has become common solve complex problems such as modeling. present research investigated ability several data. This investigation aims explore new version...
A flash flood is the most common natural hazard that endangers people’s lives, economy, and infrastructure. Watershed management planning are essential for reducing damages, particularly in residential areas, mapping flood-sensitive zones. Flash flooding an interface dynamic between geoterrain system factors such as geology, geomorphology, soil, drainage density, slope, flood, rather than only water movement from higher to lower elevation. Consequently, vulnerability floods necessitates...
Seawater intrusion (SWI) problem is encountered in almost all coastal aquifers around the globe, however with different degrees based on multiple geological, hydrological, and environmental factors. The exacerbated through anthropogenic activities, particularly excessive extraction of groundwater. Research SWI has been conducted under field laboratory settings to comprehend its underlying mechanisms explore potential mitigation techniques. This article focuses bibliometric analysis...