92.
The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et al. (Glob Environ Change 26:152–158, 2014) has been used for computing ESV. Field parameter-based ecological insecurity index (EII) has been developed for validating the EIMs along with the statistical methods. Applied Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) revealed that 21.88 to 60.79% area has predicted as highly ecologically insecure in all the selected four stone quarrying and crushing dominated clusters and this is has inflated from 2000 to 2020. All the applied models are acceptable in terms of their performances, but the RF model is found to be the best representative in relation to EII. It causes considerable loss of ESV which ranges from 160,845.18 US$ to 757,445.17 US$ in all the clusters from 2000 to 2020. The findings of the study are useful for ecological management in this area. It further recommends applying such an approach in such similar fields to establish the general finding and provides knowledge to the state of arts.
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