Summary The customary terrestrial survey involves direct measurement of the land dimension in a particular area. The information accuracy of land dimension changes person to person in the existing method. Moreover, it consumes more time to conduct an assessment and produces a precise outcome. Hence, Remote Sensing offers an effective solution for accurate measurement using the Land Satellite Image Mapping with Geographic Positioning System (GPS) values. Therefore, the proposed model concentrates on the analysis of the object using Landsat Image through categorization based on the field cover analysis. The Geographic Information System (GIS) facilitates convenient measurement to determine the land dimension along with Georeferential tag. In addition to that, the features of each object are preprocessed to reduce the noise and the acquired information is stored in the classifier. Also, the features are trained using 2‐fold validation, which will improve efficiency during detection. Finally, the soft hyperplane discriminators of Gaussian produce better separation accuracy compared to feature classes and object prediction. Furthermore, the Bag of feature model performance is improved by working on Hue, Saturation, and Intensity color map with multifeatured extractions on the sample images. The proposed model was trained with 1800 image samples of various land covers. On testing, the classifiers aggregate 91.4% accuracy on cross‐evaluation between true class and predicted class using medium Gaussian Support Vector Machine.

The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques.

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