Managing floods in interconnected nonurban and urban areas of arid regions prone to flash floods requires a more dynamic and integrated approach than traditional linear methods. In this regard, this study proposes a novel multi-stage decision-making framework which reshapes the traditional cascade approach to flood management by addressing the interdependencies between upstream and downstream regions. The proposed cyclic decision-making process involves five main steps: First, the Hydraulic and Hydrological (H/H) conditions in urban and nonurban areas were modeled using the SWMM.
View Article and Find Full Text PDFFlash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one.
View Article and Find Full Text PDFIn regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area.
View Article and Find Full Text PDFMachine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir.
View Article and Find Full Text PDFWater quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir.
View Article and Find Full Text PDFEffectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II).
View Article and Find Full Text PDFGroundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights.
View Article and Find Full Text PDFOver the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost.
View Article and Find Full Text PDFOver the past decades, urbanization in Arabian Gulf region expands in flood-prone areas at an unprecedented rate. Chronic water stress and potential changes in extreme rainfall attributed to climate change therefore pose unique challenges in planning and designing water management infrastructures. The objective of this study is to develop a framework to integrate climate change variations into intensity-duration-frequency (IDF) curves in Oman.
View Article and Find Full Text PDFThe objective of this study is to investigate how the magnitude and occurrence of extreme precipitation events are affected by climate change and to predict the subsequent impacts on the wadi flow regime in the Al-Khod catchment area, Muscat, Oman. The tank model, a lumped-parameter rainfall-runoff model, was used to simulate the wadi flow. Precipitation extremes and their potential future changes were predicted using six-member ensembles of general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5).
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