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Showing results for Ali R. naghi
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Artificial intelligence evaluation of nature based flood resilience in hilly terrain.
Mabrouk A, Inam I, Qureshi MZ, Ali T, Murtaza N, Ouda MM, Al-Naghi AAA, Cruz DT. Mabrouk A, et al. Sci Rep. 2025 Oct 10;15(1):35492. doi: 10.1038/s41598-025-19629-9. Sci Rep. 2025. PMID: 41073752 Free PMC article.
The RF model has greater prediction power compared to the SVR model Due to a higher R-value of 0.9809 for FV and 0.9906 for RV conditions. The result of SHAP analysis demonstrates greater influence of time ratio on peak discharge under FV (SHAP range: 25 and RV (SHAP range …
The RF model has greater prediction power compared to the SVR model Due to a higher R-value of 0.9809 for FV and 0.9906 for RV condit …
Experimental and machine learning prediction of compressive strength of chemically activated RHA based RAC using SHAP and PDP analysis.
Al-Naghi AAA, Ali T, Inam I, Qureshi MZ, Mahmood MS, Kahla NB, Ghazouani N. Al-Naghi AAA, et al. Sci Rep. 2025 Dec 4;15(1):43164. doi: 10.1038/s41598-025-25592-2. Sci Rep. 2025. PMID: 41345473 Free PMC article.
Among them, XGB outperformed others with an R(2) of 0.951, RMSE of 3.222 MPa, and MAE of 1.862 MPa. SHAP and Partial Dependence Plot (PDP) analyses revealed curing age, RCA, and Na(2)SO(4) content as key influencing factors. ...
Among them, XGB outperformed others with an R(2) of 0.951, RMSE of 3.222 MPa, and MAE of 1.862 MPa. SHAP and Partial Dependence Plot …