Personal profile
Education/Academic qualification
PhD, RWTH Aachen University
Award Date: 23 Sep 2003
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being -
SDG 6 Clean Water and Sanitation -
SDG 8 Decent Work and Economic Growth -
SDG 11 Sustainable Cities and Communities -
SDG 13 Climate Action -
SDG 14 Life Below Water -
SDG 15 Life on Land -
SDG 17 Partnerships for the Goals
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Dive into the research topics where Husam Musa Baalousha is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Assessing simplified approaches in modeling rainfall-induced landslides using Richards' equation with Biot poroelasticity
Abbasov, R., Fahs, M., Fontaine, V., Baalousha, H. M., Younes, A. & Toussaint, R., Jan 2026, In: Engineering Geology. 360, 108481.Research output: Contribution to journal › Article › peer-review
Open Access -
A Discrete Fracture Network Model for Coupled Variable-Density Flow and Dissolution with Dynamic Fracture Aperture Evolution
Younes, A., Baalousha, H. M., Guellouz, L. & Fahs, M., Jul 2025, In: Water (Switzerland). 17, 13, 1904.Research output: Contribution to journal › Article › peer-review
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Machine learning approaches for groundwater vulnerability assessment in arid environments: Enhancing DRASTIC with ANN and Random Forest
Baalousha, H. M., Aug 2025, In: Groundwater for Sustainable Development. 30, 101496.Research output: Contribution to journal › Article › peer-review
6 Scopus citations -
Machine Learning-Driven Calibration of MODFLOW Models: Comparing Random Forest and XGBoost Approaches
Baalousha, H. M., Aug 2025, In: Geosciences (Switzerland). 15, 8, 303.Research output: Contribution to journal › Article › peer-review
Open Access3 Scopus citations -
Machine Learning Models for Groundwater Level Prediction and Uncertainty Analysis in Ruataniwha Basin, New Zealand
Kanito, D., Benaafi, M. & Baalousha, H. M., Nov 2025, In: Hydrology. 12, 11, 282.Research output: Contribution to journal › Article › peer-review
Open Access