Ahmed El-Shafie's research while affiliated with Emirates University and other places

Publications (214)

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This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is...
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Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine lea...
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Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establi...
Preprint
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Recently, there has been increased interest in using optimization techniques to find the optimal operation for reservoirs by applying them to various aspects of the reservoir operating system, such as finding the optimal rule curves for reservoirs. The use of different algorithms (artificial bee colony (ABC), particle swarm optimization (PSO), gene...
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Reservoir inflow (Qflow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consumin...
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Silica aerogel (SA) has recently gained attention as an alternative to lightweight aggregate in lightweight cementitious composite (LCC). However, it is challenging to ensure homogenous mix of hydrophobicity SA and aqueous cementitious composite. This study investigates a novel chemical treatment of micro-sized SA using methanol solution for incorp...
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Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formu...
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The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Lévy flight int...
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This study covers the application of sim-heuristics to simulate and optimise the KLang Gate Dam (KGD) operating rule curve using the Coupled Model Intercomparison Project 5 (CMIP5) climate scenarios. This research aims to examine future climate change impacts on the KGD reservoir water resources. First, based on model institution location and data...
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The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid a...
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Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural ne...
Preprint
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Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under v...
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Precast concrete sandwich panels (PCSPs) are utilized for the external cladding of structures (i.e., residential, and commercial) due to their high thermal efficiency and adequate composite action that resist applied loads. PCSPs are composed of an insulating layer with high thermal resistance that is mechanically connected to the concrete. In the...
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Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this...
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Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro...
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Flash floods are not only the deadliest weather-related hazard but also one of the leading challenges with which governments and societies need to cope. Flash floods occur within a very limited time, which is insufficient to enable effective warnings and preparedness. Flash floods have become, for many reasons, the most frequent form of natural dis...
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The current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study’s novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the study used the RCP 8.5 scenario based on seven clima...
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Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran’s Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked...
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Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average an...
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Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial in...
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The lower number of rainfall events resulting in drier environment over the years is a crucial phenomenon attracting the concern of all around the world. The impact of rainfall deficiencies will lead to issues of water resources availability, both for the agricultural sector and also for health and human development. Therefore, this study on rainfa...
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Prediction of reference evapotranspiration (ET0) remains a challenge, especially with forward multi-step forecasting. The bottleneck facing current research is the limitation of the span of the forecasting time horizons, which can be rather disappointing, especially when long-term forecasting is desired. In this study, an explainable model structur...
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Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep l...
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Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR–vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull op...
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Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in diffe...
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Accurate and reliable optimization and simulation of the dam reservoir system to ensure optimal use of water resources cannot be achieved without precise and effective models. Providing insight into reservoir system operation and simulation modeling through a comprehensive overview of the previous studies and expanding research horizons can enhance...
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Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), arti...
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Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it’s the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitorin...
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Accurate and reliable suspended sediment load (SSL) prediction models are necessary for the planning and management of water resource structures. In this study, four machine learning techniques, namely Gradient boost regression (GBT), Random Forest (RF), Support vector machine (SVM), and Artificial neural network ANN will be developed to predict SS...
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The groundwater resources are the essential sources for irrigation and agriculture management. Forecasting groundwater levels (GWL) for the current and future periods is an essential topic of watershed management. The prediction of GWL helps prevent overexploitation. The Auto-Regressive Integrated Moving Average model (ARIMA) is a widely known line...
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Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO 4 , pH and total hardness (as CaCO 3 ) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was r...
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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations....
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Shafie (2022) Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia, Engineering Applications of Computational Fluid Mechanics, 16:1, 422-440, ABSTRACT A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with...
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Drought modelling is an important issue because it is required for curbing or mitigating its effects, alerting the people to the its consequences, and water resources planning. This study investigates the capability of a deep learning method, long short-term memory (LSTM), in forecasting drought calculated from monthly rainfall data obtained from f...
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Growing population and rapid urbanization are among the major causes of ground water level (GWL) depletion. Modeling GWL is considered as tough task as the GWL variation depends on various complex hydrological and meteorological variables. However, few methodologies have been proposed in literature for modeling GWL. The present research offers a su...
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Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algori...
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High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and devel...
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This paper analyses experimental data on sediment incipient motion with varying sediment bed thickness (of d50, 5, 10 and 24 mm). Sediment particles (with sizes ranging from 0.5 mm to 4.78 mm) were used to evaluate the effect of deposited bed. Variation of shear velocity estimation was investigated where the critical Shields parameter was expressed...
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Many studies have been conducted to reduce carbon emission on the world. Large numbers of private vehicles help to increase greenhouse gas emissions, traffic congestion, physical inactivity, high temperature and air pollution. For these reasons, an urgent need to reduce carbon emission produced by private vehicles by switching to public transport....
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Statistical drought characterization is critical for drought studies within the multivariate temporal and frequency domains. An efficient drought management can result in improved drought preparedness and risk management. In this study, Standardized precipitation indices (SPIs) over various timeframes were derived using precipitation data. The hist...
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Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging task. A reliable method that is able to extract th...
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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 mana...
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It is crucial to keep an eye on the water levels in reservoirs in order for them to perform at peak, as they are one of the, if not, the most vital part in water resource management. The water stored is essential in providing water supply, generating hydropower as well as preventing overlasting droughts. Thus, efficient forecasting models are essen...
Preprint
In this paper, simulated annealing (SA) algorithm is integrated with mayfly optimization Algorithm (MOA) as SAMOA for determination of optimal hyper parameters of support vector regression (SVR) as SVR-SAMOA to overcome the exploration weakness of MOA method. Proposed method accuracy is examined with the classical SVR and hybrid SVR-MOA. To examine...
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This paper mainly focuses on development of robust accurate streamflow prediction model by balancing the ability of exploitation and exploration to find the best parameters of a machine learning model. Therefore, simulated annealing (SA) algorithm is integrated with mayfly optimization algorithm (MOA) as SAMOA for determination of optimal hyper par...
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Water shortage in arid and semi-arid land is one of the most important challenges of decision-makers. The seawater greenhouse (SWG) is a useful solution for water supply in the agriculture sector. The optimal design of a SWG with lower consumption of energy and higher freshwater production is a real challenge for the decision-makers. This study use...
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Accuracy of reservoir inflow forecasting is an important issue for the reservoir operation and water resources management. The main aim of the current study is to develop reliable models to forecast monthly inflow data. The present research proposed a robust model called co-active neuro-fuzzy inference system (CANFIS) to improve the forecasting acc...
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Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi-reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation....
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Development, operations and management of multi-objective reservoirs, is vital for timely water supply. Optimisation studies were done at the Klang Gate Dam (KGD) utilising standard optimisation and dynamic programming; according to the technology then. Taking it further, the KGD was studied using the nature-inspired meta-heuristic algorithms (MHAs...
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This study reported the state of the art of different artificial intelligence (AI) methods for groundwater quality (GWQ) modeling and introduce a brief description of common AI approaches. In addtion a bibliographic review of practices over the past two decades, was presented and attained result were compared. More than 80 journal articles from 200...
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Forecasting of reservoir inflow is one of the most vital concerns when it comes to managing water resources at reservoirs to mitigate natural hazards such as flooding. Machine learning (ML) models have become widely prevalent in capturing the complexity of reservoir inflow time-series data. However, the model structure's selection required several...
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Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perce...
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This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells....
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Palm oil clinker (POC) aggregates is a viable alternative to the naturally occurring sand and gravel in the manufacturing of concrete. The usage of POC aggregates assists in the reduction of solid waste and preserves the consumption of natural resources. Although researchers investigated the mechanical response of POC-containing concrete, limited r...
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Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essen...
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In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of...
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Earthquake is one of the devastating and frightening natural disasters that caused big casualties in a small duration. Earthquake caused lots of damage in just a few minutes and the casualties of the earthquake increase as the population increase which also contribute to higher amount of property and buildings. Therefore, by developing model capabl...
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Streamflow prediction help the modelers to manage water resources in watersheds. It gives essential information for flood control and reservoir operation. This study uses the copula-based-Bayesian model averaging (CBMA) as an improved version of the BMA model for predicting streamflow in the Golok River, the Kelantan River, the Lanas River, and the...
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Earthquakes have been universally recognised as seismological disasters that pose a threat to civilization and need to be monitored through prediction models. The development and usage of traditional statistical predicting models, which require the understanding of underlying physical scientific processes in a system and large amounts of data prepa...
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Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO4 in...
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The significant success in benefiting the Artificial Intelligence (AI) tools in modeling and analyzing hydrological process, physical, biological and chemical process has been proved in the previous studies. The stability and flexibility of AI models is widely applied to estimate water resources variables. Generally, the advanced computing and inte...
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Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who wo...