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Aimrun Wayayok

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Articles

Machine-Learning Downscaling of GRACE/GRACE-FO for Basin-Scale Groundwater Storage Assessment in a Semi-Arid Basin

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By Aimrun Wayayok, Abba Ibrahim, Noorellimia Mat Toridi
| Preprints.org
You are already at the latest version The Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) missions provide terrestrial water storage anomalies (TWSA) at coarse spatial resolution (300 km), limiting their application in medium-sized basins. This study develops a machine-learning framework to enhance the spatial interpretability of GRACE mascon TWSA within the 48,000 km² Hadejia-Jama’are River Basin, Nigeria.
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A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

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By Zed Zulkafli, Aimrun Wayayok, Helmi Zulhaidi Mohd, Mokhalad A. Majeed
| MDPI
Altmetric share Share announcement Help format_quote Cite thumb_up ... Endorse textsms ... Comment Need Help? Support Find support for a specific problem in the support section of our website. Get Support Feedback Please let us know what you think of our products and services. Give Feedback Information Visit our dedicated information section to learn more about MDPI. Get Information clear Font Type: Arial Georgia Verdana Line Spacing:    Column Width:    Open AccessArticle by Mokhalad A.
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Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques: A Case Study Focusing on Nasiriyah City, Southern Iraq

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By Sadeq Khaleefah Hanoon, Ahmad Fikri Abdullah, Aimrun Wayayok, Sadeq Khaleefah
| MDPI
Abstract : 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 complex processes.
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