Global NEST Journal

Digital Elevation Model (DEM) is an inevitable tool for many applications, such as natural resource management. In the present scenario, it is available with various resolutions from meter to sub-meter accuracy. These data are used extensively to explore the river basin's physical features by detecting and extracting watershed boundaries, elevation points, drainage networks, flow directions, and morphological parameters. Although many researchers have used DEMs to delineate the watershed boundaries of the river basin, accuracy makes the significant distinction and usage of various DEMs. In this study, the accuracy of watershed delineation derived from various open-source DEMs such as Shuttle Radar Topographic Mission (SRTM), Advanced Spaceborne Thermal Emission, and Reflection Radiometer (ASTER), Cartosat-1, and ALOS Palsar, in the vaigai river basin have been observed. The Arc SWAT hydrological modeling tool was used to delineate the watershed boundary using the DEMs mentioned above. Eventually, the outcome is validated with the help of base data received from TN-PWD (Tamilnadu Public Work Department). The results attained from the extraction of the watershed boundary from various DEMs reveal that the watershed boundary delineated from ALOS – Palsar is closely matching with the actual boundary of vaigai river basin. The regression analysis of the watershed on area and perimeter obtained from the ALOS Palsar DEM yielded R2 as 0.97 and 0.99. This R2 exhibits the close fitness of the predicted value with data obtained from TN-PWD data. The ALOS Palsar DEM provided low vertical errors and high vertical accuracy compared to other DEMs. Overall the ALOS Palsar DEM generated outcome exhibited comparatively better results than the rest of the DEMs.

The wastes are in many forms consist of organic materials like human, medical, food, plastics, construction waste etc. many researchers are focused on the waste management to gives more importance to the environment. The bio degradable waste was used in this research with naturals fibers. The need for hybrid composites has increased in a variety of industrial applications. A unique and useful substance is created when two or more distinct components join in this kind of composite. Because of its exceptional characteristics, research is being done on the integration of particles bio waste and fiber into polymers. The like a strong weight-to-strength ratio, immunity to rust and temperature changes, and several custom qualities. This research focuses on the property description of Calotropis Gigantea and Bio waste ashes in form of epoxy matrix and in concrete as retrofitting composite. Calotropis Gigantea and Bio waste ashes are in composite form. field emission scanning electron microscope (FESEM) The characterisation is used to make sure that the polymer's structural shape and particle dispersion are correct. The mechanical, dynamic mechanical, thermal wear and concrete strength qualities are assessed. When particles are added, the qualities improve. By raising the weight of particle at certain percentage to the matrix and concrete mixture is for to increase the mechanical properties like tension, elongation, impact. To analyze the tribological behaviour, wear analysis is carried out. The results revealed that, the laminates reinforced with Calotropis Gigantea and Bio waste ashes show maximum strength. This is correlated with the increased interaction between the fiber-matrix, waste ash and homogeneous dispersion particle and also having greater strength compare to conventional concrete. The FESEM micrographs for tensile fracture surface also provided structural morphology. The findings will provide a vivid understanding for the use of composites in various structural retrofitting applications.

In coastal lagoons, eutrophication is a significant ecological and environmental problem. The majority of the pollution and deterioration problems that coastal zones and their ecosystems face are due to human influence through urbanization and industrialization. In this research, evaluations of water quality were made throughout a few lagoons in India during both the wet and dry seasons to establish their suitability for harboring aquatic life and the impact of seasonality on their quality. In order to reduce the effect of environmental influences on sensor based on nanoparticle sensitive film, an enhanced deep Back Propagation (BP) neural system was developed. The incompatibility between high performance and low calculation time was typically found in today’s modern sensor compensation techniques. Moreover, higher recognition errors were always the outcome of weak self-learning abilities. To solve these problems, a 16-layer deep BP neural network model was developed after hyper parameter searching. In Addition, the stochastic gradient descent (SGD) method and the mini-batch method were employed to achieve a proper balance between model performance and training time difficulties. The enhanced deep BP neural network was a great compensation solution for the sensor used in complicated environments due to its self-learning and self-adaptability. The proposed method is named as Imp_BPNN_SGD is analyzed in terms of various parameters and found that it achieves 35.6% of RMSE, 21.6% of MAE, 19.6% of RAE and 97.2% of D2

Urbanized areas groundwater vulnerability and contamination are serious issues that require due attention. There are other models, notably the DRASTIC models employed to assess the vulnerability of groundwater. In a modified version of the DRASTIC model known as anthropogenic influence was incorporated into DRASTICA as a model parameter. The research used a cutting-edge methodology to define the anthropogenic employing satellite data of nighttime lighting to have an impact using human settlements as a stand-in and the Palani Taluk's surrounding land use/landcover. The spatial integration of various parameter maps was done using a geographic information system. According to data on groundwater vulnerability to pollution, around 29.98% of the area is in a very high vulnerability zone, followed by 23.68% of area covers high vulnerability, 25.18% comes under the range of moderate vulnerability, 12.14% covers the area in the zones of low vulnerability, and 8.97% area covers the total area in the ranges of very low vulnerability. Utilizing the amount of nitrate in ground water, the findings were confirmed. It was determined that in an urban environment, the suggested DRASTICA model achieved enhanced than the conservative DRASTIC model. The classifications are as follows: extremely high, high, medium, and low. Very high Vulnerability zones account for 214 values in Palani taluk, the research region, whereas very low vulnerability zones account for 3 values in Palani taluk. Urbanization index showed that anthropogenic impact and the depth of the water table had a substantial impact on groundwater susceptibility to contamination, indicating that manmade effect must be carefully taken into consideration in such studies. This study's modified-DRASTIC/DRASTICA model will aid in better classifying groundwater sensitive areas to contamination where manmade pollution is high, especially in and near metropolitan centers. The groundwater susceptibility possible map that is produced can be more successfully used as the main tool for managing, organizing, and safeguarding groundwater resources. Low nitrate concentrations are found in forests and arid terrain, whereas high nitrate values are found in urban areas. The majority of the Palani taluk has low to moderate nitrate concentrations. The Melkaraipatty and Thalaiyuthu Palani Taluk's urban areas are considered by high sensitivity because of low water levels and strong manmade effect.