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- W3033341975 endingPage "1371" @default.
- W3033341975 startingPage "1351" @default.
- W3033341975 abstract "Stone dust induced risk and vulnerability in the developing nations is very thought-provoking and therefore is necessary to address scientifically to manage it. Identifying vulnerable areas using the robust method is preliminary and essential steps. The present study has intended to delineate Human health vulnerability models (HHVMs) using machine learning algorithms and justify whether ensemble prediction can provide improved results in stone quarry and crushing dominated Middle catchment of Dwarka river basin. Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Reduced Error Pruning (REP) Tree, Gaussian Process algorithms and ensemble prediction have been applied for HHV modelling. Total 6.9 to 7.37% in cluster 1, 9.11 to 9.6% in cluster 2, 19.63 to 26.79% and 5.34 to 12.85% areas in cluster 3 and 4 is found into very high human health vulnerable class as per the five applied models. REPTree model is found consistent for predicting vulnerability in case of all the clusters followed by GP model. As per the result of RMSE, the RF model is appeared as the most consistent model followed by REPTree. Some of the ensemble prediction models are potential for yielding improved result than individual algorithm." @default.
- W3033341975 created "2020-06-12" @default.
- W3033341975 creator A5005501805 @default.
- W3033341975 creator A5090825061 @default.
- W3033341975 date "2020-09-01" @default.
- W3033341975 modified "2023-09-27" @default.
- W3033341975 title "Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India" @default.
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