K S Abhilash

In recent years, airborne broadcasting has grown more prevalent in cities. Air quality degradation is a severe air pollution issue that exists daily. To forecast the amount of pollutants, Artificial Neural Network (ANN) and Linear Vector Quantization (LVQ) techniques were utilized. The data set dimensions are defined by the pre-processing procedure and the feature extraction mechanism. The ANN model predicts categorization concentration, allowing the LVQ model to classify direct situations with greater accuracy using explanatory factors. The ANN+LVQ model outperformed other technologies in terms of classification accuracy. The raw data was cleaned to improve the accuracy of the prediction algorithms. The pollutants discovered in the collection are NO2, NOx, O3, Benzene, Xylene, NH3, CO, SO2, PM10, NO, and Toluene. The performance of the recommendation and forecast models were tested in this study using two datasets in two distinct experiments. In urban, rural, and industrial settings, the proposed ANN model is successful in detecting air quality and predicting pollution levels. The ANN-LVQ model obtained 90% percent sensitivity, 97.59% accuracy, and 99.46% specificity with 2.43% error rate. The suggested model's accuracy is much greater than that of other current research models.

Wireless Sensor Networks (WSN) are being developed in the agriculture and weather station sectors to monitor and manage a number of objects. Sensor network hardware platforms are low-power embedded systems featuring a variety of sensors, such as on-board sensors and analogue I/O ports for adding sensors. The design of genuine environmental monitoring and communication systems is greatly aided by the creation of a WSN. A WSN is a collection of sensors that are geographically spread and specialized for monitoring and recording environmental variables and storing the findings in a central location. Limited power consumption as sensor nodes works on batteries, Nodes are mobile opposed to conventional wires, Usage in harsh environment, Nodes can be deployed in any fashion over an area, Easy to use and multi-layer design operation these are the main characteristics of WSN. Limits imposed on the energy storage device by their applications and deployment settings are less relevant to WSN environment-monitoring applications than controlling power consumption and optimizing data transmission. A WSN is used to record real-time traffic and environmental data in order to develop descriptive and predictive models for determining the optimum routes to reduce traffic congestion and, as a result, urban pollution. C