Analysis of environmental factors using AI and ML methods.

Area of Science:

• Environmental Science
• Data Science
• Machine Learning

Background:

• Accurate forecasting of snow cover and Normalized Difference Vegetation Index (NDVI) is crucial for hydrological models and predicting forest fire spread.
• Artificial Neural Networks (ANNs), including Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) networks, are effective for time series forecasting due to their adaptive nature.
• LSTM networks excel at learning long-term dependencies in sequential data.

Purpose of the Study:

• To apply a deep neural network model for time series forecasting of key environmental variables.
• To investigate the efficacy of the LSTM model for forecasting snow cover, temperature, and NDVI.
• To support environmental factor analysis in the Himachal Pradesh region.

Main Methods:

• A coarse-to-fine strategy was employed, involving a review of related research.
• Long Short Term Memory (LSTM) network analysis was performed on environmental data from Himachal Pradesh (2001-2017).
• The dataset included parameters such as temperature, snow cover, and vegetation index.

Main Results:

• The LSTM model was successfully applied for time series forecasting of environmental variables.
• Forecasts were generated for temperature, snow cover, and NDVI in the Himachal Pradesh region.
• The developed system offers an efficient method for assessing and improving environmental factor analysis.

Conclusions:

• Deep neural networks, specifically LSTM, provide a powerful tool for environmental time series forecasting.
• Accurate forecasting of environmental variables aids in reliable hydrological modeling and forest management.
• The implemented system enhances the efficiency of environmental factor analysis and decision-making.