Integrated Remote Sensing and Machine Learning Assessment of Environmental Degradation in Haryana (2000–2023).
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Abstract
Environmental degradation has been exacerbated in fast growing urban and agricultural areas, and integrated assessment frameworks are needed to facilitate sound policy action. This study has been carried out in the state of Haryana by adopting remote sensing, statistical and machine learning techniques to assess the environmental degradation in the state for the period 2000-2023. The composite Environmental Degradation Index (EDI) was created through Principal Component Analysis (PCA) incorporating various indices like, Normalised Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Use/Land Cover (LULC), groundwater levels and air quality parameters.
The Mann–Kendall trend analysis and Sen's slope estimator indicated that the NDVI and groundwater levels had significantly decreased while the temperature LST and particulate pollution had increased. The regression analysis showed that urbanisation and agricultural intensity were the two main factors that accounted for a significant amount of variability (R ² = 0.78) in environmental degradation. In comparison to other predictive models, the model Random Forest showed better performance (R² = 0.91), which is higher than the models Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) that were able to capture the complex non-linear relationships.
The geographical analysis revealed high degradation zones in NCR districts. Projections based on scenarios suggest an increase of 12–18% in environmental degradation, but a decrease of up to 10% is possible with targeted policy interventions. The study provides a sound, scientific basis for sustainable land-use planning, groundwater management and pollution control measures.....
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