UNIVERSITI TUNKU ABDUL RAHMAN

3CDRR

“We advance interdisciplinary research in disaster risk & climate resilience across Malaysia and beyond.”

Featured Project: Low-carbon Steel Industry Research Initiative

Congratulations to Dr. Leong Kah Hon on Securing a RM1 Million Research Grant from Masteel!

Announcement

We are proud to announce that Dr. Leong Kah Hon has successfully secured a RM1 million research grant from Masteel to support a collaborative low-carbon steel industry research initiative. This significant achievement reflects Dr. Leong’s research excellence and leadership in advancing sustainable solutions in the steel sector. The grant will be used to drive impactful research in carbon capture, utilization, and steel decarbonization strategies in partnership with industry stakeholders. Dr. Leong’s work exemplifies innovation and contributes meaningfully to both academic and industrial progress. Congratulations, Dr. Leong Kah Hon – your accomplishment brings great pride to our institution and inspires continued research excellence!

Featured Research (Published in Top 5% Journal)

The study examined multi-decadal changes in land surface temperature (LST) and urban land cover across eleven Southeast Asian capital cities from 1990 to 2024 using satellite imagery and machine learning analysis. It found widespread surface warming and intensification of urban heat island effects, particularly in rapidly expanding urban areas. Major trends included increased built-up land, loss of vegetation and water bodies, and strong positive correlations between urban expansion and elevated surface temperatures. Cities such as Vientiane, Kuala Lumpur, and Bangkok showed the most pronounced warming. These findings highlight the climatic consequences of urbanisation and provide empirical evidence to support climate-adaptive planning in the region.


This review article presents a comprehensive overview of advanced methods for estimating evapotranspiration (ET): a key hydrological process critical for water resource management and climate resilience. The study evaluates how remote sensing satellite data, combined with machine learning and artificial intelligence techniques, enhances ET estimation over large areas where meteorological data are limited. It discusses important satellite sources, essential image processing steps, key environmental parameters (like land surface temperature and vegetation indices), and emerging AI models for scalable geospatial analysis. The review also outlines challenges and future opportunities to improve accuracy and applicability in sustainable water and disaster risk management contexts.

This comprehensive review examines how climate change affects hydropower systems worldwide, synthesizing current methodologies, modelling approaches, key limitations, and recommendations for future assessments. The study highlights critical challenges such as data scarcity, model uncertainties, and the complex interactions between climate drivers and hydrological responses. To strengthen assessments, it recommends enhanced data collection, advanced monitoring systems, and the use of ensemble modelling and machine learning techniques to better capture nonlinear system behaviours. The insights aim to support policymakers and energy planners in developing resilient strategies for managing hydropower infrastructure under changing climatic conditions.