Analysis of environmental factors using AI and ML methods.

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.

Cloud and IoT based smart architecture for desalination water treatment.

Increasing water demand and the deteriorating environment has continuously stressed the requirement for new technology and methods to attain optimized use of resources and desalination management, converting seawater into pure drinking water. In this age, the Internet of Things use allows us to optimize a series of previously complicated processes to perform and required enormous resources. One of these is optimizing the management of water treatment. This research presents an implementable water treatment model and suggests smart environment that can control water treatment plants. The proposed system gathers data and analysing to provide the most efficient approach for water desalination operations. The desalination framework integrates smart enabling technologies such as Cloud Portal, Network communication, Internet of Things, Sensors powered by solar energy with ancient water purification as part of seawater's desalination project. The proposed framework incorporates the new-age technologies, which are essential for efficient and effective operations of desalination systems. The implemented desalination dual membrane framework uses solar energy for purifying saline water using ancient methods to produce clean water for drinking and irrigation. The desalination produced 0.47 m3/l of freshwater from a saline concentration of 10 g/l, consuming 8.31 KWh/m3 energy for production from the prototype implementation, which makes desalination process cost effective.