Exploration
Naman Chandel; Sushindra Kumar Gupta; Anand Kumar Ravi
Abstract
Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can ...
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Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can be used to predict groundwater quality and leachate characteristics to manage this issue efficiently. This study proposes a machine learning-based model for the prediction of groundwater quality and leachate characteristics using the effective water quality index (EWQI). The leachate dataset used in this study was obtained from a landfill site, and the groundwater quality dataset was collected from literature review. The mean values of TDS, Ca, Mg, NO3-, and PO4- exceeded the prescribed limit for drinking water purposes. The proposed model utilizes a machine learning architecture based on a convolutional neural network (CNN) to extract relevant features from the input data. The extracted features are then fed into a fully connected network to estimate the EWQI of the input samples. The model, trained and tested on leachate and groundwater quality datasets, achieves a high accuracy and computational efficiency, aiding in predicting groundwater quality and leachate characteristics for waste management.
Kaustubh Sinha; Priyangi Sharma; Kanwarpreet Singh; Sushindra Kumar Gupta; Abhishek Sharma
Abstract
Land surface temperature (LST) is one of the most important geological features of any area in the present times. During the study, the information regarding the land surface temperature is calculated using the Arc-GIS software. The LANDSAT 8 (2022) and LANDSAT 4-5 (2001 and 2011) satellite images are ...
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Land surface temperature (LST) is one of the most important geological features of any area in the present times. During the study, the information regarding the land surface temperature is calculated using the Arc-GIS software. The LANDSAT 8 (2022) and LANDSAT 4-5 (2001 and 2011) satellite images are used for the calculation of LST. From the LST maps of years 2001 and 2011, a significant rise is noticed; this is due to the rapid increment in the population of the said area. A gradual increment in the LST is present between the second period of 2011-2022. A connection between the LST and the specific humidity has also been drawn in this aspect. The specific humidity in the region has seen a significant increment in the concerned time period. Overall, it is observed that the LST of the area has increased rapidly from the -12 ˚C minimum temperature in 2001 to 27 ˚C in 2022; this is because of the human activity in the area, which has ultimately catered towards the degradation of the climatic condition and environment like LST.
