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Interview with Hafsa, Aeman, National Researcher, International Water Management Institute (IWM), CGIAR

In the interview, Hafsa Aeman discusses her passion for integrating water resource management with space technologies. She uses remote sensing and AI to tackle challenges like seawater intrusion and coastal erosion, focusing on vulnerable coastal ecosystems. By leveraging satellite data, her work provides critical insights for sustainable water management, crucial for communities impacted by climate change. Ms Aeman highlights the significant role of space technology in water management, especially through remote sensing, which helps monitor precipitation, soil moisture, and groundwater levels. Her proudest achievement is a publication on seawater intrusion, recognized for its innovative use of AI and remote sensing, contributing to Pakistan’s Living Indus initiative. At the International Water Management Institute (IWMI), Hafsa’s research integrates AI and remote sensing to optimize water and irrigation management systems. She emphasizes the importance of addressing seawater intrusion, which poses threats to agriculture, ecosystems, and global food security. She also underscores the role of community engagement in sustainable water management through capacity-building workshops for farmers, promoting smarter irrigation practices. She advocates for leadership opportunities for young scientists and believes AI can revolutionize water management by enabling more accurate and efficient data analysis. Rain, symbolizing renewal and sustenance, is her favorite aggregate state of water.

Interview with Hafsa, Aeman, National Researcher, International Water Management Institute (IWM), CGIAR

In the interview, Hafsa Aeman discusses her passion for integrating water resource management with space technologies. She uses remote sensing and AI to tackle challenges like seawater intrusion and coastal erosion, focusing on vulnerable coastal ecosystems. By leveraging satellite data, her work provides critical insights for sustainable water management, crucial for communities impacted by climate change. Ms Aeman highlights the significant role of space technology in water management, especially through remote sensing, which helps monitor precipitation, soil moisture, and groundwater levels. Her proudest achievement is a publication on seawater intrusion, recognized for its innovative use of AI and remote sensing, contributing to Pakistan’s Living Indus initiative. At the International Water Management Institute (IWMI), Hafsa’s research integrates AI and remote sensing to optimize water and irrigation management systems. She emphasizes the importance of addressing seawater intrusion, which poses threats to agriculture, ecosystems, and global food security. She also underscores the role of community engagement in sustainable water management through capacity-building workshops for farmers, promoting smarter irrigation practices. She advocates for leadership opportunities for young scientists and believes AI can revolutionize water management by enabling more accurate and efficient data analysis. Rain, symbolizing renewal and sustenance, is her favorite aggregate state of water.

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Impact of sea level rise on LULC in Bahrain

Human-caused elements of climate change are the leading contributors to the presently observed phenomenon of global sea level rise. According to the United Nations Framework Convention on Climate Change (UNFCCC) Report, the impacts of sea level rise on the Kingdom of Bahrain are exacerbated as it is small island nation with low-lying lands. To provide a comprehensive understanding of these impacts, remote sensing technologies have been employed to model sea level rise and to classify the land to be affected.

Oil Spill Detection System in the Arabian Gulf Region: An Azure Machine-Learning Approach

Locating oil spills is a crucial portion of an effective marine contamination administration. In this project, we address the issue of oil spillage location exposure within the Arabian Gulf region, by leveraging a Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Custom Vision). Our workflow comprises a virtual machine, a database, and four modules (an Information Collection Module, a Discovery Show, an Application Module, and a Choice Module).

Stakeholder

Margosa Environmental Solutions

Margosa develops pioneering geodata solutions using advanced open source technologies and machine learning frameworks, which enables the integration of massive environmental and GIS datasets. We offer cost-effective, scalable, and secure global data analytics platforms for government, multilateral, corporate, educational, and not-for-profit institutions. Our mission is to transform complex natural resource information into practical knowledge for decision-makers and stakeholders alike.