Journal Article

Machine learning models for wetland habitat vulnerability in mature Ganges delta

Debanshi, Sandipta, Pal, Swades

2021

Overview

The present study attempts to measure wetland habitat vulnerability (WHV) in the Indian part of mature Ganges delta. Predictive algorithms belonging to bivariate statistics and machine learning (ML) algorithms were applied for fulfilling the data mining and generating the models. Results show that 60% of the wetland areas are covered by moderate to very high WHV, out of which > 300 km 2 belong to very high WHV followed by a high vulnerability in almost 150 km 2 . This areal coverage increases by 10–15% from phase II to phase III. On the other hand, a relatively safe situation is confined to < 200 km 2 . The receiver operating characteristic curve, root-mean-square error, and correlation coefficient are used to assess the accuracy of these models and categorization of habitat vulnerability. Ensemble modeling is done using the individual models having a greater accuracy level in order to increase accuracy. A field-based model of the same is prepared by gathering information directly from the field which also exhibits similar results with the algorithm-based models. Analysis of residuals in standard regression strongly supports the relevance of the selected parameters and multi-parametric models.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Accuracy / Algorithms / Aquatic habitats / Aquatic Pollution / Atmospheric Protection/Air Quality Control/Air Pollution / Bivariate analysis / Correlation coefficient / Correlation coefficients / Data mining / Earth and Environmental Science / Ecosystem / Ecotoxicology / Environment / Environmental Chemistry / Environmental Health / Environmental science / Habitats / Learning algorithms / Machine Learning / Model accuracy / Regression analysis / Research Article / river deltas / Statistical analysis / statistics / Waste Water Technology / Water Management / Water Pollution Control / Wetlands