Analysis of environmental factors using AI and ML methods

• Published in: Scientific reports Vol. 12; no. 1; p. 13267
• Main Authors: Haq, Mohd Anul, Ahmed, Ahsan, Khan, Ilyas, Gyani, Jayadev, Mohamed, Abdullah, Attia, El-Awady, Mangan, Pandian, Pandi, Dinagarapandi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-08-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 704/106; 704/106/694; 704/172; 704/172/4081; Environmental factors; Forecasting; Humanities and Social Sciences; Hydrologic models; Long short-term memory; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Snow; Snow cover; Time series; Vegetation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16665-7

The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.