Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

Wind power produces more electricity than any other form of renewable energy in the United Kingdom (UK) and plays a key role in decarbonisation of the grid. Although wind energy is seen as a sustainable alternative to fossil fuels, there are still several environmental impacts associated with all stages of the lifecycle of a wind farm. This study determined the material composition for wind turbines for various sizes and designs and the prevalence of such turbines over time, to accurately quantify waste generation following wind turbine decommissioning in the UK. The end of life stage is becoming increasingly important as a rapid rise in installation rates suggests an equally rapid rise in decommissioning rates can be expected as wind turbines reach the end of their 20–25-year operational lifetime. Waste data analytics were applied in this study for the UK in 5-year intervals, stemming from 2000 to 2039. Current practices for end of life waste management procedures have been analysed to create baseline scenarios. These scenarios have been used to explore potential waste management mitigation options for various materials and components such as reuse, remanufacture, recycling, and heat recovery from incineration. Six scenarios were then developed based on these waste management options, which have demonstrated the significant environmental benefits of such practices through quantification of waste reduction and greenhouse gas (GHG) emissions savings. For the 2015–2019 time period, over 35 kilotonnes of waste are expected to be generated annually. Overall waste is expected to increase over time to more than 1200 kilotonnes annually by 2039. Concrete is expected to account for the majority of waste associated with wind turbine decommissioning initially due to foundations for onshore turbines accounting for approximately 80% of their total weight. By 2035–2039, steel waste is expected to account for almost 50% of overall waste due to the emergence of offshore turbines, the foundations of which are predominantly made of steel.

The water supply system plays a major role in the community. The water source is carefully selected based on quality, quantity, and reliability. The quality of water at its sources is continuously deteriorating due to various anthropogenic activities and is a major concern to public health as well. The Kalu River is one of the major water resources in Sri Lanka that supplies potable water to the Kalutara district (a highly populated area) and Rathnapura district. But, there has been no significant research or investigation to examine anthropogenic activities in the river. Due to this, it is difficult to find any proper study related to the overall water quality in the Kalu River. Therefore, this study covers a crucial part related to the water quality of the Kalu River. The spatiotemporal variation of river water quality is highly important not only to processing any treatment activities but also to implementing policy decisions. In this context, water quality management is a global concern as countries strive to meet the United Nations Sustainable Development Goal 6, which aims to ensure the availability and sustainable management of water and sanitation for all. Poor water quality can have severe consequences on human health, ecosystems, and economies. Contaminated water sources pose risks of waterborne diseases, reduced agricultural productivity, and ecological imbalances. Hence, assessing and improving water quality is crucial for achieving sustainable development worldwide. Therefore, this paper presents a comprehensive analysis of spatiotemporal analysis of the water quality of the Kalu River using the water quality data of eight locations for 6 years from 2017 to 2023. Nine water quality parameters, including the pH, electrical conductivity, temperature, chemical oxygen demand, biological oxygen demand, total nitrate, total phosphate, total sulfate, total chlorine, and hardness, were used to develop a simple equation to investigate the water quality index (WQI) of the river. Higher WQI values were not recorded near the famous Kalutara Bridge throughout the years, even though the area is highly urbanized and toured due to religious importance. Overall, the water quality of the river can be considered acceptable based on the results of the WQI. The country lockdowns due to COVID-19 might have impacted the results in 2020; this can be clearly seen with the variation of the annual WQI average, as it clearly indicates decreased levels of the WQI in the years 2020 and 2021, and again, the rise of the WQI level in 2022, as this time period corresponds to the lockdown season and relaxation of the lockdown season in the country. Somehow, for most cases in the Kalu River, the WQI level is well below 25, which can be considered acceptable and suitable for human purposes. But, it may need some attention towards the areas to find possible reasons that are not in the range. Nevertheless, the results suggest the importance of continuous water quality monitoring in the Kalu River.

Microbial fuel cell (MFC) technology represents a form of renewable energy that generates bioelectricity from what would otherwise be considered a waste stream. MFCs may be ideally suited to the small island developing state (SIDS) context, such as Trinidad and Tobago where seawater as the main electrolyte is readily available, and economically renewable and sustainable electricity is also deemed a priority. Hence this project tested two identical laboratory-scaled MFC systems that were specifically designed and developed for the Caribbean regional context. They consisted of two separate chambers: an anaerobic anodic chamber inoculated with wastewater and an aerobic cathodic chamber separated by a proton exchange membrane. Domestic wastewater from two various wastewater treatment plants inflow (after screening) was placed into the anodic chamber, and seawater from the Atlantic Ocean and Gulf of Paria placed into the cathodic chambers, respectively, with the bacteria present in the wastewater attached to the anode. Experimental results demonstrated that the bacterial degradation of the wastewaters as substrate induced an electron flow through the electrodes producing bioelectricity whilst simultaneously reducing the organic matter as biochemical oxygen demand and chemical oxygen demand by 30 to 75 %. The average bioenergy output for both systems was 84 and 96 mW/m², respectively. This study demonstrated the potential for simultaneous bioenergy production and wastewater treatment in the SIDS context.