Search

Your search keyword '"Abdo, Hazem Ghassan"' showing total 263 results
Start Over Author "Abdo, Hazem Ghassan"
263 results on '"Abdo, Hazem Ghassan"'

41. Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India.

Author

Abijith, Devanantham, Saravanan, Subbarayan, Parthasarathy, K S S, Reddy, Nagireddy Masthan, Niraimathi, Janardhanam, Bindajam, Ahmed Ali, Mallick, Javed, Alharbi, Maged Muteb, and Abdo, Hazem Ghassan

Subjects
CLIMATE change, RANDOM forest algorithms, NATURAL disasters, EARTH sciences, PHYSICAL geography, FLOOD risk
Abstract

Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

48. A Novel Integrated Approach to Assess Groundwater Appropriateness for Agricultural Uses in the Eastern Coastal Region of India.

Author

Kaliyappan, Shunmuga Priya, Hasher, Fahdah Falah ben, Abdo, Hazem Ghassan, Sajil Kumar, Pazhuparambil Jayarajan, and Paneerselvam, Balamurugan

Subjects
GROUNDWATER quality, WATER management, ELECTRIC conductivity, MACHINE learning, IRRIGATION
Abstract

Due to the increase in demand for water, the rapid growth of urbanization and industrialization is the main threat to the source and quality of groundwater. The present study aimed to assess the suitability of groundwater for agricultural purposes in coastal regions using integrated approaches such as the saltwater mixing index (SWMI), the mineral saturation index (MSI), the agriculture suitability index (ASI), and unsupervised machine learning (USML) techniques. The result of the SWMI revealed that 20 and 17 sample locations were highly affected by saltwater intrusion in the study region's northern and southeastern parts during the pre- and post-monsoon seasons. The detailed analysis of electrical conductivity in groundwater revealed that 19.64% and 14.29% of the samples were unfit for irrigation purposes, especially five sample locations, during both seasons. Regarding the overall suitability of groundwater for irrigation uses, the ASI values divulged that 8.9% of the samples were unsuitable for irrigation purposes. The spatial analysis of the ASI value indicated that 43.19 and 85.33 sq. km of area were unsuitable for irrigation practices. Additionally, the USML techniques identified the most influenced parameters such as Ca2+, Mg2+, Cl−, and SO42− during both seasons. The present study results help maintain proper, sustainable water management in the study region. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results