Global NEST Journal

Flood management is the act of determining the frequency, magnitude, and duration of flood episodes based on the elements of a river basin that may be monitored. Floods endanger human lives and inflict significant property damage. It is essential for creating suitable flood risk management plans, lowering flood dangers, and evacuating people from flood-prone locations. Hydrodynamic methods for managing floods may be replaced with deep learning. Existing methods, however, concentrate on employing CNNs or RNNs to capture either the spatial or temporal flood patterns. Despite several advancements in flood control technology, less focus has been placed on minimising the damage that these systems do to the environment in order to boost their dependability and effectiveness. When the data is skewed, CNNs might overfit. We demonstrate how automated regularisation and uncertainty quantification allows Bayesian-CNN to get beyond these drawbacks. We have created a unique method to make use of the uncertainties that the Bayesian-CNN provides, which greatly improves performance on a big portion of the test data (around a 6% increase in accuracy on 77% of test data). By projecting the data into a low-dimensional space using a nonlinear dimensionality reduction approach, we also provide an entirely novel rationale for the uncertainty. This dimensionality reduction makes it possible to visualize the test data for interpretation and displays the data's structure in a low-dimensional feature space. This paper discusses and makes use of uncertainties for flood control while demonstrating the benefits of Bayesian-CNN over state-of-the-art technology. As a consequence, the Bayesian-CNN obtains 95.7% F1-score, 99.3% F1-score, 98.5% precision, and 98.3% recall.

Changes in weather patterns and the global climate are a result of global warming, which has been linked to rising carbon dioxide concentrations. Understanding the mechanisms on earth that have an impact on atmospheric carbon concentrations and its cycling—collectively referred to as the "Carbon cycle"—is essential for estimating carbon budgets. Studies on carbon budgets have largely ignored the role of inland waterways in the carbon cycle, yet rivers play an active role by absorbing/releasing carbon into the atmosphere and transferring dissolved carbon into oceans. In tropical parts of the planet, it is anticipated that water bodies, like as rivers, would contribute to carbon emissions to a greater extent newline Although there are many studies on the impact of human activity on the Ganga River, there are very few that take an ecological viewpoint. In light of these facts, the following was designed as the study's hypothesis: newline The Ganga River has positive feedbacks as a result of human activity, which alters the environment at a level significantly greater than what has previously been projected.The current investigation was conducted in a significant tropical, non-perennial river of the Ganga. In order to calculate the carbon flow of the Ganga river newline, it is important to understand the interaction between geological processes and the concentration of different ions in groundwater and surface water.

As the production of biomass waste from agroindustry grows across the world, a large amount of agro-based ashes ends up in polluting land. India produced 405.4 million tones of sugarcane in 2019, with a capacity of 105.4 million tonnes of bagasse and 2.5 million tonnes of SBA. The utilization of such ashes as SCMs contributes to a fixing of issues related to CO2 emissions from cement industry and land pollution in agro-based industry. Although many research has been performed on the utilization of Sugarcane Bagasse Ash (SBA) and limestone in concrete, research on their combined usage in the concrete is limited. As a result, the current work involves the performance evaluation of a ternary blended concrete incorporating SBA and limestone. The blended concrete's workability properties, compressive strength, water absorption, RCPT, Sorptivity, water permeability and electrical resistivity are examined in this paper. It was observed that a minimum of 10-15% limestone along with 10% SBA was adequate to increase the concrete performance, however exceeding 15 percent had a detrimental impact on concrete properties. In summary, utilization of SBA and limestone powder reduces cement consumption, reduce environmental impact due to SBA and cement, improves the economy, and increases the sustainability of concrete production. It improves the compressive strength and durability properties of ternary blended concrete.

Recently, there has been a rise in research based on data-driven based machine learning models to address the issue of processing speed posed by various physiological simulations. The majority of studies, however, have concentrated on creating models for particular water sources, drainage systems, and gauge stations area. Therefore, unless further data is supplied and the models are subsequently trained, their outcomes cannot be effectively applied to different scenarios. With its excellent spatio-temporal accuracy range with the advanced analytics, the forthcoming Global Navigations Satellite Systems (GNSSs) technology offers a novel method for the monitoring of flood disasters in dynamic manner. This study recommends a Multi-layered Neural Network (MNN) for flood monitoring, which combines a Convolutional Neural Networking (CNN) along with a backward propagation based (BP) learning network. The CNN component is used to obtain the abstracted features from full delay doppler maping images. The BP section is provided with GNSSs characteristics, such as reflection of surface with power range, and also the vegetation attributes from the Soil Moistening Active Passives (SMAP) satellite source data. In comparison to physiologically based models, the results shown that the CNN-oriented system makes accurate predictions on "unseen" areas of catchment with a substantially shorter processing time. The obtained results imply that, with respect to accuracy of prediction, the patch-enabled option is superior to the resizing-based option. Additionally, every experiment has demonstrated that the forecast of flow speed is more precise than the prognosis of water depth, indicating that the water deposition is more complex to information about global level elevations than the flow speed with velocity.