ORCID Profile
0000-0002-3338-0092
Current Organisation
University of British Columbia
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Publisher: MDPI AG
Date: 10-09-2019
DOI: 10.3390/W11091880
Abstract: : Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.
Publisher: MDPI AG
Date: 28-07-2023
DOI: 10.3390/RS15153759
Abstract: The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: MDPI AG
Date: 26-11-2021
DOI: 10.3390/RS13234795
Abstract: Whitings, the manifestation of high levels of suspended fine-grained calcium carbonate particles in the water, have been reported and studied worldwide. However, the triggering mechanism of whiting occurrences remains uncertain. The current study attempted to analyze potential factors that might account for whiting occurrences in a semi-enclosed gulf (namely the Arabian/Persian Gulf, hereinafter called the Gulf). First, spatial and temporal variability of whiting events and different potential driving factors (i.e., whiting seasonality, wind-induced mixing, sea surface temperature, and bathymetry) were explored and examined for five years (2015–2020). Second, as a general indicator of whiting occurrences in the Gulf, a whiting index (WI) was developed using time-series analysis and decision tree (DT) classification algorithm. Third, the correlation between the proposed WI and the spatial coverage of various whiting events was examined. Time-series analysis showed that whiting events during the winter season are associated with high winds that lasted for several days. Nevertheless, whiting events were rarely observed despite high wind speed and increased potential for CaCO3 precipitation in summer. This finding suggests that wind-driven forces might be potential sources for mixing water columns, resuspension of CaCO3 particles, and the appearance of whiting in the Gulf. The DT classification algorithm demonstrated that a minimum WI value of 1.1 can explain the initiation of most summer and winter whiting events. Furthermore, a Pearson correlation coefficient of 0.73 was measured between WI and the extent of whiting along the UAE and Qatar coastlines in the Gulf. The proposed WI shows a simple yet effective method for identifying and estimating the extent of whiting in the Gulf.
Publisher: MDPI AG
Date: 24-04-2023
DOI: 10.3390/RS15092248
Abstract: Among all the natural hazards, earthquake prediction is an arduous task. Although many studies have been published on earthquake hazard assessment (EHA), very few have been published on the use of artificial intelligence (AI) in spatial probability assessment (SPA). There is a great deal of complexity observed in the SPA modeling process due to the involvement of seismological to geophysical factors. Recent studies have shown that the insertion of certain integrated factors such as ground shaking, seismic gap, and tectonic contacts in the AI model improves accuracy to a great extent. Because of the black-box nature of AI models, this paper explores the use of an explainable artificial intelligence (XAI) model in SPA. This study aims to develop a hybrid Inception v3-ensemble extreme gradient boosting (XGBoost) model and shapely additive explanations (SHAP). The model would efficiently interpret and recognize factors’ behavior and their weighted contribution. The work explains the specific factors responsible for and their importance in SPA. The earthquake inventory data were collected from the US Geological Survey (USGS) for the past 22 years ranging the magnitudes from 5 Mw and above. Landsat-8 satellite imagery and digital elevation model (DEM) data were also incorporated in the analysis. Results revealed that the SHAP outputs align with the hybrid Inception v3-XGBoost model (87.9% accuracy) explanations, thus indicating the necessity to add new factors such as seismic gaps and tectonic contacts, where the absence of these factors makes the prediction model performs poorly. According to SHAP interpretations, peak ground accelerations (PGA), magnitude variation, seismic gap, and epicenter density are the most critical factors for SPA. The recent Turkey earthquakes (Mw 7.8, 7.5, and 6.7) due to the active east Anatolian fault validate the obtained AI-based earthquake SPA results. The conclusions drawn from the explainable algorithm depicted the importance of relevant, irrelevant, and new futuristic factors in AI-based SPA modeling.
Publisher: Elsevier BV
Date: 04-2022
Publisher: Springer Science and Business Media LLC
Date: 19-10-2019
DOI: 10.1007/S10661-019-7854-9
Abstract: The negative consequences of urbanisation have been recently recognised despite the social and economic benefits it provides to the community. Effects of urbanisation include increases in surface runoff, frequency and magnitude of floods and urban water harvesting capacity. Accordingly, this study utilised multi-spectral and multi-resolution satellite images combined with field data to conduct a quantitative assessment of the impact of urbanisation on urban flooding for the period of 1975-2015 in Ajman City, United Arab Emirates (UAE). Results showed that urban areas in the city have increased by approximately 12-fold over the period 1975-2015, whilst the population increased by approximately 16-fold. Owing to a substantial increase in urbanisation (as impervious areas expanded), minimum precipitation to generate runoff in built areas dropped from approximately 16.37 mm in 1975 to approximately 13.3 mm in 2015, which caused a substantial increase in the surface runoff. To visualise the flooding potential, urban flooding maps were generated using a well-established decision analysis technique called Analytical Hierarchy Process. The latter adopted three thematic factors, namely excess rain, elevation and slope. Flooding potential was then found to have increased substantially, specifically in the downtown area. Finally, this study is expected to contribute highly to flood protection and sustainable urban storm water management in Ajman City.
Location: United Arab Emirates
No related grants have been discovered for Mohamad Khalil.