Improving Landslide Susceptibility Mapping via Non-Landslide Sampling Strategies Israa Fadhil Ibraheem, Mastura Azmi, Muhammad Wafiy Adli Ramli Engineering Technology and Applied Science Research, 2026 This study assesses the impact of non-landslide sample selection on landslide susceptibility mapping. Two strategies were compared: (i) randomly selecting negatives outside buffers around mapped landslides (S1) and (ii) a targeted, threshold-and-buffer method (S2). A dataset from Sulaymaniyah Governorate, Iraq, including 148 landslides and 434 non-landslide points, was modeled using Logistic Regression (LG) with 14 conditioning factors derived in ArcGIS. Factors were ranked using the Frequency Ratio (FR). The five most influential ones—slope, Topographic Wetness Index (TWI), soil, Normalized Difference Vegetation Index (NDVI), and Land Use Land Cover (LULC)—were binary-reclassified to delineate safe zones. Additionally, S2-negative samples were collected within a 500–750 m annulus. Performance was evaluated using confusion matrices and Receiver Operating Characteristic – Area Under the Curve (ROC–AUC) on a 75/25 split. S2 achieved accuracy of 89.6%, precision of 77.5%, and AUC of 93.7%, outperforming S1 (85.4%, 70.0%, 91.2%). When validation was limited to landslide points, S1 exhibited a slightly higher AUC (88.4% vs. 85.3%), indicating greater sensitivity but lower precision. Results show that threshold-guided, proximity-constrained negatives enhance class separation and lower false positives without altering the learning algorithm, supporting their application for more reliable susceptibility mapping.
From Data Design to Map Footprint: Quantifying Sampling Effects on Landslide Susceptibility International Journal of Intelligent Engineering and Systems, 2026 This study evaluates how non-landslide (background) sampling strategy and the landslide: non-landslide (LS: NLS) ratio influence the performance and reliability of landslide susceptibility models.Five learners-Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and a Convolutional Neural Network (CNN)-were trained and tested under five dataset regimes.These differed by sampling design (random vs. selective/curated), representation density (single point vs. multi-point per landslide polygon), and LS:NLS ratio (1:1 and 1:3).A unified pipeline was implemented in which preprocessing, stratified crossvalidation, isotonic probability calibration, and Youden-J threshold optimization were held constant across models.Model performance was assessed using ROC-AUC, accuracy, precision, recall, -coefficient, and reliability metrics including the Brier score.Spatial leakage was quantified through distance-to-nearest training point statistics to evaluate potential over-optimism caused by clustered sampling.Results show that curated background sampling and balanced class ratios (particularly 1:1 datasets) improved calibration and reduced operating-point bias, while random or imbalanced designs required more permissive decision thresholds to maintain recall.Despite differences in learning architecture, the models converged on physically meaningful controls, notably slope-aspect-relief interactions, proximity to roads and faults, soil type, and antecedent moisture conditions.Basin-scale hydrological indices contributed to smaller but systematic gains.Overall, the findings demonstrate that sampling design exerts as much influence on susceptibility mapping as the choice of algorithm, and that calibrated probabilities are essential for producing reliable and operationally useful landslide susceptibility maps.The study highlights the need for transparent reporting of sampling regimes, spatial independence diagnostics, and probability calibration when developing and comparing landslide susceptibility models.
A Comprehensive Documentation of Orchid Species in Kedah, Malaysia Azimah, Farah Alia Nordin, Muhammad Wafiy Adli Ramli, Nurhafizul Abu Seri Pertanika Journal of Tropical Agricultural Science, 2025 The documentation of orchid species in Kedah, Malaysia, highlights a remarkable biodiversity characterized by 130 species spread across five subfamilies and 61 genera. Significant ecological areas like Gunung Jerai, Ulu Muda Forest Reserve, and Langkawi UNESCO Global Geopark serve as critical habitats, hosting rare and endemic species such as Paphiopedilum exul and Spathoglottis hardingiana. Advanced methodologies, including DNA barcoding and micromorphological analysis, have been utilized to enhance taxonomic precision and elucidate evolutionary relationships among these orchids. Despite these advances, orchid diversity in Kedah faces serious threats from habitat loss due to deforestation, climate change, and illegal poaching. These anthropogenic pressures underline the pressing need for effective conservation strategies. The study identifies limestone hills and montane forests as biodiversity hotspots that require urgent protection. By promoting sustainable practices and addressing habitat degradation, this research underscores the vital importance of integrated conservation strategies to safeguard the unique orchid flora of Kedah for future generations. Furthermore, it advocates for increased awareness, community involvement, and the establishment of a centralized database to facilitate ongoing research and conservation efforts. Ultimately, this documentation serves as a foundational resource for future taxonomic studies, ecological assessments, and biodiversity conservation initiatives, contributing to policy recommendations aimed at preserving and restoring Kedah’s rich orchid heritage.
AI for Environmental Sustainability in Urban Areas Ilyas Ahmad Huqqani, Mohd Amirul Mahamud, Mohd Azmeer Abu Bakar, Muhammad Wafiy Adli Ramli, Tan Mou Leong, Narimah Samat AI Driven Strategies for Inclusive and Sustainable Urbanization, 2025 Urban areas encounter serious environmental issues, such as pollution, resource depletion, waste buildup, and climate change effects, caused by rapid urban growth and increasing populations. Addressing these environmental issues is important for achieving sustainable urban development. Artificial intelligence (AI) provides new, data-based solutions to tackle these problems and encourage environmental sustainability. AI is key in improving energy management with smart grids, cutting emissions using smart transportation, refining waste management, and aiding sustainable city planning through predictive modeling. This topic explores the role of AI in promoting environmental sustainability in urban areas. Furthermore, this topic also discussed how AI can aid in the evolution of urban regions that are not only more ecologically sustainable, such as by enhancing air and water quality assessment and refining waste management practices, but also incorporate greater green infrastructure within the constructed environment and are consequently capable of real-time responses to their interactions with the natural ecosystem.
Enhancing urban resilience: disaster preparedness and community engagement in Selangor, Malaysia Muhammad Wafiy Adli Ramli, Nor Eliza Alias, Yusrin Faiz Abdul Wahab, Zulfaqar Sa’adi, Azimah Abd Rahman, Shazwin Mat Taib, Mohd Azmeer Abu Bakar International Journal of Disaster Resilience in the Built Environment, 2025 Purpose The purpose of this study is to investigate disaster preparedness in urban communities across three districts in Selangor, Malaysia. The focus is on awareness of flood early warning systems (EWSs), financial preparedness, evacuation knowledge and community engagement in urban community resilience. Design/methodology/approach A questionnaire was distributed to residents of Hulu Langat, Kuala Langat and Sepang districts. The data collected were analyzed using descriptive statistics and Chi-square tests to determine significant associations between variables such as EWS awareness and participation in emergency drills. Findings This study reveals a significant correlation between awareness of EWSs and participation in emergency drills, highlighting the importance of informed communities for effective disaster response. However, financial preparedness is a critical gap, with only 25% of respondents having emergency savings and 15% subscribing to disaster insurance. These findings underscore the need for enhanced educational programs and financial planning initiatives to improve urban disaster resilience. Originality/value This research contributes to the field of disaster risk management by providing a detailed analysis of urban community preparedness in Selangor, Malaysia. This study highlights the critical areas needing improvement, such as financial preparedness and community engagement, and offers practical recommendations for policymakers and emergency planners. This study’s focus on urban resilience aligns with Sustainable Development Goal 11 and provides valuable insights for enhancing disaster preparedness in rapidly urbanizing regions.
Spatial uncertainty in monthly rainfall projections for Peninsular Malaysia: Assessment of the top-performing GCMs under worst case scenario Zulfaqar Sa’adi, Nor Eliza Alias, Zulkifli Yusop, Nurzalikha Sa’adi, Mohd Hadi Akbar Basri, Muhammad Wafiy Adli Ramli, Mohamad Rajab Houmsi, Shamsuddin Shahid, Dinesh Kumar Iop Conference Series Earth and Environmental Science, 2025 This research investigates spatial uncertainty in monthly rainfall projections for Peninsular Malaysia (PM) by assessing the performance of top-performing General Circulation Models (GCMs) under a high-emission future scenario of SSP5-8.5. Despite the widespread use of GCMs, their accuracy in simulating rainfall for PM remains uncertain due to complex topography and diverse climates, and this study addresses this gap by enhancing GCM reliability understanding, identifying uncertainties, and providing a framework for future performance assessments. Utilizing Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall data from 1981 to 2023, this study evaluates 27 monthly Atmosphere-Ocean GCMs with a 100 km nominal resolution. The compromise programming index (CPI) and Fisher Jenks method refine the selection process, categorizing models into distinct clusters based on performance. Through rigorous ranking processes using multiple statistical metrics, including Mean Absolute Error (MAE), Normalized Root Mean Square Error (NRMSE), Percent Bias (PBIAS), Ratio of the Root Mean Square Error to the Standard Deviation of the Observations (RSR), Nash-Sutcliffe Efficiency (NSE), Modified Degree of Agreement (MD), Coefficient of Persistence (CP), Coefficient of Determination (R2), and Kling-Gupta Efficiency (KGE), top-performing GCMs such as CMCC-ESM2 and CMCC-CM2-SR5 are identified. Evaluation based on probability distribution function (PDF), cumulative distribution function (CDF), and various statistical metrics indicates that CMCC-ESM2 generally provides more accurate and reliable projections. Specifically, the MAE ranges from 75.59 to 145.49, while the NRMSE spans 94.2 to 166. The PBIAS is observed between -27.6 and 43.4, and the RSR ranges from 0.71 to 1.77. Furthermore, the CP varies from - 1.94 to 0.32, and the R2 values range from -0.06 to 0.43. Additionally, the KGE ranges from 0.02 to 0.51, and the VE falls between 0.35 and 0.68. These metrics collectively reinforce the reliability of CMCC-ESM2’s projections. Subsequently, spatial projections for the near-future period of 2021-2060 and the far-future period of 2061-2100 are analyzed separately. The significance of this study is in quantifying the degree of spatial reliability of GCMs across PM. Through comprehensive statistical assessments, this study identifies GCMs that offer more accurate and consistent rainfall projections across PM’s diverse geography, enhancing spatial reliability for improved climate predictions and regional adaptation planning.
A new approach on landslide vulnerability assessment and landslide risk index for critical infrastructures in Malaysia Malaysian Construction Research Journal, 2021
Forecasting Kemaman River Water Level Using Hybrid ARIMA-STL Mode V Someetheram, MF Marsani, MW Adli, MRM Salleh, B Badyalina Malaysian Journal of Fundamental and Applied Sciences 22 (1), 36-50 , 2026 2026
Improving Landslide Susceptibility Mapping via Non-Landslide Sampling Strategies IF Ibraheem, M Azmi, MWA Ramli Engineering, Technology & Applied Science Research 16 (1), 30829-30834 , 2026 2026
Performance assessment of three long-term gridded precipitation products for precipitation and extreme event analysis in the Padas River Basin, Malaysia CK Chang, SM Desa, Z Feng, Y San Liew, MAM Ariff, MWA Ramli, ML Tan Advances in Space Research 77 (2), 1830-1844 , 2026 2026
From Data Design to Map Footprint: Quantifying Sampling Effects on Landslide Susceptibility I Israa Fadhil, A Mastura, R Muhammad Wafiy Adli International Journal of Intelligent Engineering and Systems 19 (3), 745 - 758 , 2026 2026
Assessing future drought extremes and their impact on hydropower vulnerability in Sarawak Z Sa’adi, Z Yusop, NE Alias, ZZ Noor, RA Kemarau, MWA Ramli, ... Chemical Engineering Transactions 122, 175-180 , 2025 2025 Citations: 8
Enhancing urban resilience: disaster preparedness and community engagement in Selangor, Malaysia MWA Ramli, NE Alias, YF Abdul Wahab, Z Sa’adi, A Abd Rahman, ... International Journal of Disaster Resilience in the Built Environment 16 (5 … , 2025 2025 Citations: 3
AI for Environmental Sustainability in Urban Areas IA Huqqani, MA Mahamud, MAA Bakar, MWA Ramli, TM Leong, N Samat AI-Driven Strategies for Inclusive and Sustainable Urbanization, 82-90 , 2025 2025
AI for Environmental Sustainability IA Huqqani, MA Mahamud, MAA Bakar, MWA Ramli, TM Leong, N Samat AI-Driven Strategies for Inclusive and Sustainable Urbanization, 82 , 2025 2025
A Comprehensive Documentation of Orchid Species in Kedah, Malaysia. A Abd Rahman, FA Nordin, MWA Ramli, N Abu Seri Pertanika Journal of Tropical Agricultural Science 48 (5) , 2025 2025
Disaster risk assessment in Malaysia: current state, challenges, and future directions MW Adli Ramli, NE Alias, Z Sa’adi, YF Abdul Wahab, AABD Rahman Natural Hazards 121 (12), 13875-13898 , 2025 2025 Citations: 3
Spatial uncertainty in monthly rainfall projections for Peninsular Malaysia: assessment of the top-performing GCMs under worst case scenario Z Sa’adi, NE Alias, Z Yusop, N Sa’adi, MHA Basri, MWA Ramli, ... IOP Conference Series: Earth and Environmental Science 1505 (1), 012014 , 2025 2025 Citations: 1
A hybrid machine learning-based past performance and envelope approach for rainfall projection in Sarawak, Malaysia Z Sa'adi, S Shahid, MS Shiru, K Ahmed, M Alamgir, MR Houmsi, ... Urban Climate 61, 102442 , 2025 2025 Citations: 2
Analysis of Precipitation Patterns in the East Coast States of Peninsular Malaysia from 1981 to 2019 MSN bin Rezali, MWA Ramli, MAA Bakar Journal of Asian Geography 4 (1), 71-81 , 2025 2025 Citations: 2
Clustering and Significance in Spatial Distribution Analysis of the Skudai River Catchment Using Geographic Information System (GIS) MAH Saidi, MWA Ramli, MAA Bakar, MA Mahamud, WMMW Ibrahim Journal of Asian Geography 4 (1), 20-26 , 2025 2025
Evaluating the Influence of La Niña on Tropical Greening in Borneo Through Geographically Weighted Regression Z Saadi, ZZ Noor, MWA Ramli, N Sa’adi, NNK Phượng Journal of Advanced Geospatial Science & Technology 5 (1), 57-87 , 2025 2025
Geospatial analysis of NDVI-rainfall dynamics under high ENSO influence in Peninsular Malaysia Z Saadi, NE Alias, Z Yusop, LS Mazilamani, MR Houmsi, LN Houmsi, ... Journal of Advanced Geospatial Science & Technology 5 (1), 1-33 , 2025 2025 Citations: 2
Integrating Local Knowledge into Floods Impact Assessment: A Case Study from Gemereh, Segamat, Johor MAAB Mohamad Afnan Haikal Saidi, Muhammad Wafiy Adli Ramli, Nor Eliza Alias ... e-Bangi: Journal of Social Sciences & Humanities 22 (2), 514 - 525 , 2025 2025 Citations: 1
Community perception and environmental effects of flood mitigation plans in Segamat, Johor MWA Ramli, NE Alias, Z Yusop GADING (Online) Journal for the Social Sciences 28 (1), 1-10 , 2025 2025
Multi-hazard, multidimensional disaster risk validation in selangor’s three urban districts, Malaysia MWA Ramli, NE Alias, H Mohd Yusof, YF Abdul Wahab, Z Sa’adi, ... Geomatics, Natural Hazards and Risk 15 (1), 2413683 , 2024 2024 Citations: 4
Evaluating flood early warning system and public preparedness and knowledge in urban and semi-urban areas of Johor, Malaysia: Challenges and opportunities Z Sa’adi, MWA Ramli, WANWA Tajuddin, NZ Arman, CHC Hassan, ... International Journal of Disaster Risk Reduction 113, 104870 , 2024 2024 Citations: 11
MOST CITED SCHOLAR PUBLICATIONS
Community responses on effective flood dissemination warnings—A case study of the December 2014 Kelantan Flood, Malaysia NE Alias, NA Salim, SM Taib, MB Mohd Yusof, R Saari, MW Adli Ramli, ... Journal of flood risk management 13, e12552 , 2020 2020 Citations: 50
Disaster risk index: a review of local scale concept and methodologies MWA Ramli, NE Alias, Z Yusop, SM Taib IOP conference series: earth and environmental science 479 (1), 012023 , 2020 2020 Citations: 36
Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia Z Sa'adi, NE Alias, Z Yusop, Z Iqbal, MR Houmsi, LN Houmsi, MWA Ramli, ... Science of The Total Environment 912, 169187 , 2024 2024 Citations: 33
Application of CHIRPS dataset in the selection of rain-based indices for drought assessments in Johor River Basin, Malaysia Z Sa'adi, Z Yusop, NE Alias, MS Shiru, MKI Muhammad, MWA Ramli Science of the Total Environment 892, 164471 , 2023 2023 Citations: 33
Development of a Local, Integrated Disaster Risk Assessment Framework for Malaysia MWA Ramli, NE Alias, H Mohd Yusof, Z Yusop, S Mat Taib Sustainability 19 (MDPI) , 2021 2021 Citations: 33
Spatial multidimensional vulnerability assessment index in urban area-A case study Selangor, Malaysia MWA Ramli, NE Alias, HM Yusof, Z Yusop, SM Taib, YFA Wahab, ... Progress in Disaster Science 20, 100296 , 2023 2023 Citations: 26
Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia Z Sa’adi, Z Yusop, NE Alias, MF Chow, MKI Muhammad, MWA Ramli, ... Applied Computing and Geosciences 20, 100145 , 2023 2023 Citations: 21
CHIRPS rainfall product application for analyzing rainfall concentration and seasonality in Johor river basin, Malaysia Z Sa’adi, NE Alias, Z Yusop, MWA Ramli, MKI Muhammad Journal of Atmospheric and Solar-Terrestrial Physics 256, 106203 , 2024 2024 Citations: 14
Spatiotemporal assessment of rainfall and drought projection for integrated dam management in Benut River Basin, Malaysia under CMIP6 scenarios Z Sa'adi, NE Alias, Z Yusop, MF Chow, MKI Muhammad, LS Mazilamani, ... Environmental Challenges 15, 100892 , 2024 2024 Citations: 12
Evaluating flood early warning system and public preparedness and knowledge in urban and semi-urban areas of Johor, Malaysia: Challenges and opportunities Z Sa’adi, MWA Ramli, WANWA Tajuddin, NZ Arman, CHC Hassan, ... International Journal of Disaster Risk Reduction 113, 104870 , 2024 2024 Citations: 11
Assessing future drought extremes and their impact on hydropower vulnerability in Sarawak Z Sa’adi, Z Yusop, NE Alias, ZZ Noor, RA Kemarau, MWA Ramli, ... Chemical Engineering Transactions 122, 175-180 , 2025 2025 Citations: 8
Translation, validation and psychometric properties of Bahasa Malaysia version of the Depression Anxiety and Stress Scales (DASS). ASEAN J Psychiatry 2007; 8 (2): 82-9 M Ramli, MF Ariff, Z Zaini 2013 Citations: 6
Multidimensional Vulnerability Mapping Using GIS and Catastrophe Theory MWA Ramli, NE Alias International Journal of Geoinformatics 20 (8), 1 - 16 , 2024 2024 Citations: 5
Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia. Applied Computing and Geosciences, 20, 100145 Z Sa’adi, Z Yusop, NE Alias, MF Chow, MKI Muhammad, MWA Ramli, ... 2023 Citations: 5
Influence of dam to rainfall-runoff response in a tropical climate–A case study of Selangor River Basin, Malaysia AF Bahar, Z Yusop, NE Alias, MWA Ramli IOP Conference Series: Materials Science and Engineering 1153 (1), 012004 , 2021 2021 Citations: 5
Evaluating transportation modes and routes for disaster relief in Kelantan using geographical information system MWA Ramli, NE Alias, SM Taib 2018 Citations: 5
Multi-hazard, multidimensional disaster risk validation in selangor’s three urban districts, Malaysia MWA Ramli, NE Alias, H Mohd Yusof, YF Abdul Wahab, Z Sa’adi, ... Geomatics, Natural Hazards and Risk 15 (1), 2413683 , 2024 2024 Citations: 4
Effect of flue gas recirculation on multi-cyclones performance in reducing particulate emission from palm oil mill boiler WC Chong, M Rashid, M Ramli, J Ruwaida, ZZ Noor Particulate Science and Technology 32 (3), 291-297 , 2014 2014 Citations: 4
Enhancing urban resilience: disaster preparedness and community engagement in Selangor, Malaysia MWA Ramli, NE Alias, YF Abdul Wahab, Z Sa’adi, A Abd Rahman, ... International Journal of Disaster Resilience in the Built Environment 16 (5 … , 2025 2025 Citations: 3
Disaster risk assessment in Malaysia: current state, challenges, and future directions MW Adli Ramli, NE Alias, Z Sa’adi, YF Abdul Wahab, AABD Rahman Natural Hazards 121 (12), 13875-13898 , 2025 2025 Citations: 3
