Effect of endogenous and anthropogenic factors on the alkalinisation and salinisation of freshwater in United States by using explainable machine learning
Open accessAbstract
Freshwater salinisation and alkalinisation strongly depend on human and natural factors. We used an explainable machine learning approach to investigate the impact of natural and human factors on the salinity and alkalinity in rivers in the United States. The model achieved coefficient of determination of 0.89 for salinity predictions and 0.92 for alkalinity predictions. Shapley additive explanations revealed salinisation is driven mainly by human factors like population density (18 %) and impervious surface percentage (13 %) and natural factors like run-off (14 %). The alkalinisation is mainly influenced by natural processes such as runoff (36 %) and soil-pH (20 %).
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Little research has applied XAI to deep learning models in the context of extreme heat prediction.
“While much work has been done to integrate XAI with machine learning models [26, 37], there has been little research into applying XAI to DL models, particularly in the context of extreme heat prediction.”
From Extreme heat prediction through deep learning and explainable AI (2025) · cites this paper as [37]
Cites
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
Ranasinghe, R. S. S., Kulasooriya, W. K. V. J. B., Perera, U. S., Ekanayake, I. U., Meddage, D. P. P., Mohotti, D., & Rathanayake, U. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102503 · Cited by 51 · see citing works
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Extreme heat prediction through deep learning and explainable AI
Shafiq, F., Zafar, A., Ghani Khan, M. U., Iqbal, S., Albesher, A. S., & Asghar, M. N. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316367 · Cited by 8 · see citing works
“Deep SHAP computes the SHAP values for each component using the compositional nature of neural networks to provide estimates of SHAP insights for the entire network [42,43].”
- Authors
- Wimalagunarathna, N. D., Dharmarathne, G., Ekanayake, I. U., Rathanayake, U., Alwatugoda, J., & Meddage, D. P. P.
- Affiliations
- University of Sri Jayewardenepura (LK)
- RMIT University (AU)
- Atlantic Technological University (IE)
- UNSW Canberra (AU)
- Corresponding author
- Meddage, D.P.P.
- Venue
- Case Studies in Chemical and Environmental Engineering
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 10
- Pages
- 100919
- ISSN
- 2666-0164 (ISSN-L)
- OpenAlex
- W4401945496
- Cited by
- 8
- References
- 1
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1
- FWCI
- 0.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Case Studies in Chemical and Environmental Engineering (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality and Pollution Assessment
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality and Resources Studies
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- anthropogenic factors, internal factors, explainable machine learning, water quality, United States
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