The effectiveness of ground penetrating radar (GPR) in identifying and categorizing diseases that occur underground beneath the surfaces of urban roads is investigated in this study. Both 2D and 3D forward modeling use simulation with the GprMax program to show the response characteristics of common cavity illnesses, which facilitates interpretation in practical situations. The cavity morphology classification accuracy is improved to 90.5% by using convolutional neural networks (CNNs), specifically transfer learning with AlexNet. This method outperforms existing approaches even with minimal data. Four primary types are identified from an analysis of 1965 subsurface cavity data: hollow bodies, empty bodies, loose bodies, and water-rich bodies. These categories are important for evaluating road risks such as voids and subsidence. However, it is still difficult to interpret picture features linked to cavity diseases accurately because of a variety of elements, such as anthropogenic, environmental, and geological influences. However, the accurate interpretation and recognition of image features related to cavity diseases remain challenging. Moreover, there are various factors involved in the formation of underground diseases and cavities, including geological and environmental factors, physical and chemical properties of the geotechnical materials, anthropogenic engineering activity and social population or commercial effects.

The availability of clean and safe drinking water sources has come under scrutiny due to the degradation of water quality caused by a variety of anthropogenic and natural factors. In Nepal's Central Terai region, water quality is crucial to sustaining the environment and public health, 25 samples of shallow groundwater a thorough analysis were carried out to evaluate the quality of groundwater in Central Terai, Nepal, where it is crucial for fulfilling these basic requirements. To assist the integrated groundwater quality index of the study area the groundwater quality index such as Sodium Adsorption Ratio (SAR), Magnesium Absorption Ratio (MAR), Sodium Percentage (Na%), Permeability index (PI), Kelly’s ratio (KR), Irrigation water quality index (IWQI) and Entropy Weighted Water Quality Index (EWQI) was determine which sows the value of water quality is moderate (76%) to highly (16%) vulnerable of the study area. The discovery that a number of chemical characteristics, including calcium, magnesium, iron, phosphate, sodium, potassium, and salt, exceeded WHO criteria raises concerns since it may be the result of uncontrolled fertiliser usage and geological interactions. One encouraging finding was that the EWQI determined that most groundwater samples were fit for human consumption. A small percentage of the samples, nonetheless, did not meet the suggested standards, underscoring the importance of exercising caution while safeguarding this essential resource. The IWQI showed that several of the samples ware suitable when it came to irrigation. The use of Artificial Neural Network (ANN) for the prediction of the integrated groundwater quality index vulnerability is significant with the validation value R=0.998 and the regression coefficient (R²) is 0.996. To stop the quality of the groundwater from further declining and to safeguard public health, these zones need specific solutions. For, the purpose of developing efficient plans for managing water resources and mitigating causes of pollution, this research is a great resource for those in charge of making decisions in the area.

The effectiveness of ground penetrating radar (GPR) in identifying and categorizing diseases that occur underground beneath the surfaces of urban roads is investigated in this study. Both 2D and 3D forward modeling use simulation with the GprMax program to show the response characteristics of common cavity illnesses, which facilitates interpretation in practical situations. The cavity morphology classification accuracy is improved to 90.5% by using convolutional neural networks (CNNs), specifically transfer learning with AlexNet. This method outperforms existing approaches even with minimal data. Four primary types are identified from an analysis of 1965 subsurface cavity data: hollow bodies, empty bodies, loose bodies, and water-rich bodies. These categories are important for evaluating road risks such as voids and subsidence. However, it is still difficult to interpret picture features linked to cavity diseases accurately because of a variety of elements, such as anthropogenic, environmental, and geological influences. However, the accurate interpretation and recognition of image features related to cavity diseases remain challenging. Moreover, there are various factors involved in the formation of underground diseases and cavities, including geological and environmental factors, physical and chemical properties of the geotechnical materials, anthropogenic engineering activity and social population or commercial effects.