Zaher Mundher Yaseen

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Verified

Zaher verified their affiliation via an institutional email.

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• PhD in Civil Engineering
• Assistant Professor at King Fahd University of Petroleum and Minerals

Drought is India's foremost concern because of global warming, low precipitation, deforestation, and temperature variation. Madhya Pradesh is the central state of India, where drought risks are gradually recorded as high vulnerability due to low rainfall and water shortage. Drought monitoring indices like the standardized precipitation evapotranspi...

This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress. Two field experiments were conducted with three irrigation regimes: 100% (B100), 75% (B75), and 50% (B50) of the full irrigation requirements. Various measured parameters includin...

Rapid climate change is amplifying the frequency and severity of global flooding events. These floods induce declines in agricultural areas, water bodies, barren lands, precipitating diminished crop productivity due to habitat loss and constrained water availability. Conversely, urban sprawl, notably within high-risk flood zones, exhibits substanti...

Relative humidity (RH) is among the water cycle’s important parameters and stochastic processes. Accurate estimation of RH is essential for numerous water resources management tasks. In this study, five ensemble machine-learning models including CatBoost, random forest (RF), AdaBoost, extreme gradient boost (XGB), and Gaussian processing regression...

We investigate a fractional energy supply–demand system (ES–DS) model using power-law-type kernels and advanced operators called fractal-fractional operators with a couple of fractal and fractional orders. It is established that for the fractal-fractional model of the ES–DS, a solution exists and it is unique. One of the principal innovations is to...

The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasin...

Malaysia’s coastline is incessantly exposed to coastal hazards, sea-level rise (SLR), and coastal erosion. A quantitative examination of shoreline migration patterns over various timeframes is necessary to comprehend land–sea interface behaviour and coastal ecology. Due to gradual changes in sea currents and coastline, Langkawi International Airpor...

This study aimed to assess and predict the surface water quality of Manzala Lake, Egypt, and identify the geo-environmental factors affecting its ecosystem. An Aquatic Water Quality Index (AWQI) was developed alongside four pollution indices (PIs): Heavy Metal Pollution Index (HPI), Pollution Index (PI), Contamination Index (CI), and Metal Index (M...

Floods and erosion are natural hazards that present a substantial risks to both human and ecological systems, particularly in coastal regions. Flooding occurs when water inundates typically dry areas, causing widespread damage, while erosion gradually depletes soil and rock through processes driven by water and wind. This study proposes an innovati...

The contamination of water and soils with heavy metals poses a significant environmental threat, making the development of effective removal strategies a global priority. Hence, the determination of heavy metals can play an essential role in environmental monitoring and assessment. In the current research, ensemble machine learning (ML) models (i.e...

Accurate prediction of spatially dependent functional data is critical for various engineering and scientific applications. In this study, a spatial functional deep neural network model was developed with a novel non-linear modeling framework that seamlessly integrates spatial dependencies and functional predictors using deep learning techniques. T...

An accurate assessment of shale gas resources is highly important for the sustainable development of these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental for understanding the distribution and quality of hydrocarbon source rocks within a shale gas reservoir. The elevation of the TOC is often associated with the prese...

Nanofluids play a crucial role in the advancement of everyday life. Nanomaterials can be used in an inclusive choice of applications, including recovering oil, melting electronic components in gadgets, air conditioning fluid development, cooling spirals, engineering and production, heat storing equipment, and bioengineering. Thus, the work focuses...

The precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins. Achieving this objective necessitates the development of highly accurate river water level forecasts. Hence, a novel hybrid model is provided, incorporating singular value decompo...

Drought assessment is inherently complex, particularly under the influences of climate change, which complicates long-term forecasting. This study introduces a novel hybrid deep learning model, Deep Feedforward Natural Networks (DFFNN), enhanced by War Strategy Optimization (WSO), aimed at forecasting the Standardized Precipitation Evapotranspirati...

Air pollution, especially small particulate matter (PM₂.₅), has emerged as a significant public health crisis in Pakistan, yet its long-term health impacts remain understudied. There is a critical lack of high-resolution spatiotemporal analysis that captures the changing exposure levels and associated mortality trends over extended periods. This st...

In this paper, we introduce and analyze an inertial viscosity forward–backward splitting approach. We approximate a common solution of the monotone variational inclusion problem by using the demicontractive mapping in a real Hilbert space. It is shown that the sequence produced by our suggested algorithm has a strong convergence to a solution obtai...

Streamflow (Qflow) process is one of the complex stochastic processes in the hydrology cycle owing to its associated non‐linearity and non‐stationarity characteristics. It is an essential hydrological process to address the complex time series nonlinear phenomena. In this research, a novel approach was proposed by integrating an autoregressive cond...

Hybrid concentrating solar power (CSP) plants with thermal energy storage (TES) and biomass backup enhance solar energy reliability and efficiency. TES provides energy during low sunlight or high demand, while biomass provides continuous heat generation when TES is depleted. Therefore, the current study developed three tree optimizers (fine, medium...

Iraq is one of the five countries most affected by high temperatures, low precipitation, drought, and desertification hazards. In this research, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) images of Basra, southern Iraq, were used from 1986 to 2021. The relationships between Land Surface T...

The impact of solar ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and the prediction of ultraviolet index (UVI) is considere...

Hybrid organic Rankine cycle (HORC) is a hydrodynamic plant used from industrial processes for low-temperature heat sources, such as geothermal, solar, and waste heat. Intelligent models were developed to predict the first and the second thermodynamic efficiencies and the levelized energy cost to optimize the overall thermal and economic efficiency...

Pollution monitoring in surface water using field observational procedure is a challenging matter as it is time consuming, and needs a lot of efforts. This study addresses the challenge of efficiently monitoring and predicting water pollution using a GIS-based artificial neural network (ANN) to detect heavy metal (HM) pollution in surface water and...

A remarkable efficiency of 27.88% was achieved for hierarchical 2D/3D/2D perovskite solar cells using Dion–Jacobson and Ruddlesden–Popper 2D layers.

Integrating artificial intelligence (AI) into energy management using phase change materials (PCMs) is a revolutionary approach to improving building energy efficiency. This strategy aims to maximize the coefficient of performance (COP) of chillers to tackle the pressing issues of energy peak demand and increasing costs. In order to address the int...

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (C...

This study extensively compares CMIP5 and CMIP6 models in simulating historical and projected annual and seasonal climate variability over Nigeria. Thirteen Global Climate Models (GCMs) from both CMIPs were considered and compared for two future projections of the radiative concentration pathways (RCP 4.5 and 8.5) and that of shared socioeconomic p...

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In the evolving field of industrial intelligent robot manufacturing, efficient production scheduling is crucial, particularly in scenarios where workshops operate independently due to varying demands, a situation described as “No Collaboration in Workshops” (NCW). This study addresses NCW inefficiencies by proposing a bi-level programming model to...

The identification of the oilfield production flow rate, which is a function of the wellhead pressure, where both are characterized as a complex, nonlinear stochastic dynamical system and heterogeneity phase coupling with a very high delay time. Hence, such a characterization of the system will not be able to fulfil the purpose of creating a conven...

This research evaluates the performance of deep learning (DL) models in predicting rainfall in George Town, Penang, utilizing the open-source NASA POWER meteorological data, which includes variables such as rainfall, dew point, solar radiation, wind speed, relative humidity, and temperature. This study introduces a newly developed hybrid DL based o...

In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Maritime potato crop system i.e., Carbon dioxide (CO2), nitrous oxide (N2O), and water vapour (H2O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological an...

To develop agricultural risk management strategies, the early identification of water deficits during the growing cycle is critical. This research proposes a deep learning hybrid approach for multi-step soil moisture forecasting in the Bundaberg region in Queensland, Australia, with predictions made for 1-day, 14-day, and 30-day, intervals. The mod...

To develop agricultural risk management strategies, the early identification of water deficits during the growing cycle is critical. This research proposes a deep learning hybrid approach for multi-step soil moisture forecasting in the Bundaberg region in Queensland, Australia, with predictions made for 1-day, 14-day, and 30-day, intervals. The mod...

Global life-threatening weather conditions and sea level rise (SLR) increasingly impact coastal landforms and increase shoreline change. The island ecosystem is affected by many natural hazards, including flooding, saltwater intrusion, vegetation degradation, shoreline change and population growth. Simultaneously, urbanization and ecological divers...

Perovskite solar cells (PSCs) have earned considerable attention as a promising photovoltaic technology for future power generation. Spiro derivatives are often used as a hole-transporting layer (HTL) in highly efficient PSC devices. However, spiro derivatives have low hole mobility and conductivity in their native form and require complicated prep...

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid...

In this study, changes in westerly waves and their connections to increased global warming under the influence of greenhouse gases were investigated via a Caputo fractional four-dimensional atmospheric system. The idea of the existence of chaotic behavior in the westerly wind's motion was depicted. It has been noted that westerlies are becoming str...

Global climate change and landform alteration are correlated with a high impact on rainfall, land surface temperature (LST), vegetation conditions, and soil moisture. This study examines rainfall, temperature, vegetation condition, moisture, and drought index to identify the decadal change from 2002 to 2021 using Landsat and global Standardized Pre...

Accurate multiphase flowing bottom-hole pressure prediction within wellbores is a critical requirement to improve tube design and production optimization. Existing models often struggle to achieve reliable accuracy across the full range of operational conditions encountered in oil and gas wells. This can lead to misallocating resources during well...

This review was conducted to highlight the most influential factors and specify the trends reducing uncertainty and increasing the accuracy of soil and water assessment tool (SWAT)-based hydrological models. Although the resolution of input data on the results of SWAT-based hydrological models has been extensively determined. There is still a gap i...

This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computat...

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile...

The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time incr...

In this study, the dynamics of a novel three-species food chain model featuring the Sokol–Howell functional response are explored. The fear of predators is incorporated into prey reproduction, and refuge is integrated into the middle predators within the framework of the Caputo fractional derivative. Theoretical aspects such as the existence and un...

Drought stands as a highly perilous natural catastrophe that impacts numerous facets of human existence. Drought data is nonstationary and noisy, posing challenges for accurate forecasting. This study proposes a novel hybrid framework integrating TVF-EMD preprocessing, LASSO feature selection and Ensemble Deep RVFL modeling for improved multistep a...

This study utilizes decision tree algorithms to estimate the financial feasibility of concentrated solar power (CSP). The main focus of CSP is on solar tower (ST) technology combined with two backup systems, such as biomass boilers and thermal energy storage (TES). The main goal is to develop three decision tree algorithms to predict the power plan...

Researchers are exploring ways to strategically redistribute energy use in cooling systems, particularly by flattening peak demand to achieve energy efficiency, cost savings, and reduce carbon footprint. This study proposes a novel approach using multiagent deep clustering reinforcement learning (MADCRL) to optimize load-shifting within multi-tank...

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with...

The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims to develop novel empirical models to enhance evapotranspiration (ET0) estimation accuracy in Nigeria. The coefficients of non-linear equations were optimised using the par...

Accurate estimation of thermophysical properties of hybrid nanofluids, such as thermal conductivity (THC), viscosity, and heat transfer performance (HTP), is crucial in energy applications and heat transfer. In this research, a comprehensive experimental and computational investigation has been performed to predict the heat transfer performance (HT...

Increasing microplastic (MP) pollution, mainly by anthropogenic sources such as plastic film mulching, waste degradation, and agricultural practices, has emerged as a demanding global environmental concern. This review examines the direct and indirect effects of MPs on crops, both in isolation and in conjunction with other contaminants, to elucidat...

The main determinants of climate change, forest dynamics, land alteration, heat stress, ecological disturbances, and urban expansion. Addressing challenges posed by increasing anthropogenic activities due to population growth and rapid urbanization requires implementing appropriate solutions and fostering greater awareness, leading to improved, hea...

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variab...

Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and prediction of the ultimate compressive strength of...

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electr...

This study examines the spatial and temporal changes in temperature and precipitation extremes from 1951 to 2020 in the West African transboundary Benue River Basin, which frequently experiences impacts from extreme events. The existing studies in the region have relied on sparse observation data, hindering a comprehensive understanding of the spat...

This study aimed to explore the level of heavy metal contamination in the soil of agricultural and industrial areas in the Eastern Province of Saudi Arabia. It adopted a novel approach integrating multiple disciplines including sampling, laboratory analysis, spatial analysis, and risk evaluation. The research focused on pinpointing levels of contam...