The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH₄ recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

Accident costs are an important component of external costs of traffic, a substantial part is related to fatal accidents. The evaluation of fatal accident costs crucially depends on the availability of an estimate for the economic value of a statistical life. This paper aims to develop a model for road accident through systems dynamics approach. To build an accident model, various factors causing the road accident and cost were identified. This model is capable of calculating the accident rate and its costs for the future. In this study the accident caused by bus alone is considered. The cost model is dealt more in this study as it requires more complex assessment. The accident model is built on the year 2000 data and predicted the accidents up to 2020 for every 5‐year interval. The accident model is valuated by comparing the predicted and actual accident data for the year 2005. Three scenarios were studied by changing the income growth rate and discount rate. Finally, best scenario is suggested for implementation. The outcome of the study is highly useful for the planners, administrators and police to make their decisions effectively for road safety investment projects.

An attempt has been made in this paper to analyze the efficiency of an intelligent fuzzy controller (IFLC) on three tank level system. Analysis of the effects studied through computer simulation using Matlab/Simulink toolbox show that the application of IFLC appears to be encouraging in the sense that it is robust in disturbance rejection under various conditions.

Pixel based Medical Image Fusion Techniques using Discrete Wavelet Transform and Stationary Wavelet Transform