Ahmad Fikri Abdullah
Biographic Data
| ID | 7463350 |
|---|---|
| NAME | Ahmad Fikri Abdullah |
| GIVEN NAMES | Ahmad Fikri |
| FAMILY NAME | Abdullah |
| SIGNATURE | ABDULLAH A F |
| AFFILIATIONS | Universiti Putra Malaysia |
| ORCID | 0000-0003-3589-1459 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques
Land use and land cover changes driven by urban sprawl has accelerated the degradation of ecosystem services in metropolitan settlements. However, most optimisation techniques do not consider the dynamic effect of urban sprawl on the spatial criteria on which decisions are based. In addition, integrating the current simulation approach with land use optimisation approaches to make a sustainable decision regarding the suitable site encompasses com…
A Novel Approach Based on Machine Learning and Public Engagement to Predict Water-Scarcity Risk in Urban Areas
Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (…
Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach
An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s kno…
No prominent works on this page.
A Novel Approach Based on Machine Learning and Public Engagement to Predict Water-Scarcity Risk in Urban Areas
Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (…
Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach
An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s kno…
Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques
Land use and land cover changes driven by urban sprawl has accelerated the degradation of ecosystem services in metropolitan settlements. However, most optimisation techniques do not consider the dynamic effect of urban sprawl on the spatial criteria on which decisions are based. In addition, integrating the current simulation approach with land use optimisation approaches to make a sustainable decision regarding the suitable site encompasses com…
Artificial Intelligence (3 works) · Artificial neural network (3 works) · Computer Science (3 works) · Geography (3 works) · Machine learning (3 works) · Support vector machine (3 works) · Engineering (2 works) · Land Use and Ecosystem Services (2 works) · Multilayer perceptron (2 works) · AdaBoost (1 work)