Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983–2014

AI and climate resilience governance

While artificial intelligence (AI) offers promising solutions to address climate change impacts, it also raises many application limitations and challenges. A risk governance perspective is used to analyze the role of AI in supporting decision-making for climate adaptation, spanning risk assessment, policy analysis, and implementation. This comprehensive review combines expert insights and systematic literature review. The study's findings indicate a large emphasis on applying AI to climate "risk assessments," particularly regarding hazard and exposure assessment, but a lack of innovative approaches and tools to evaluate resilience and vulnerability as well as prioritization and implementation process, all of which involve subjective, qualitative, and context-specific elements. Additionally, the study points out challenges such as difficulty of simulating complex long-term changes, and evolving policies and human behavior, reliance on data quality and computational resources, and the need for improved interpretability of results as areas requiring further development.

Improved feed forward with bald eagle search for conjunctive water management in deficit region

Due to increasing requirements on water resources and a lower recharge rate, the farming seasons are a vital season for the management of groundwater and surface water resource management. This condition necessitates the use of combined water distribution to meet the full water requirements. Analysis of existing surface water resources and related restrictions, this research suggested an algorithm for aquifer stabilization and fulfilling optimum water requirements. To manage the optimum withdrawals and the subsequent drop, this technique first employed the MODFLOW model for simulating the water levels. Next, an improved feed-forward neural network (IFFNN) was combined with an optimization method to create a machine learning (ML) framework. During the last phase, the findings of the optimized connectives approach as well as the relevant fields technologies to determine using improved bald eagle search with least square SVM(IBES-LSSVM) method that predicted the level of water deficit for every period, especially during farming seasons. This approach is based on an improved bald eagle search (IBES) optimization technique for finding the best settings for a least-squares support vector machine (LSSVM). The findings revealed that between 2005 and 2020, the year with the biggest water deficit was 2018 when only roughly 64 percent of water need was satisfied by groundwater (69 percent) and surface water (64 percent) (33 percent). The water depth may have risen by around 0.7 m during the study period if the optimum model had been used. The outcome of this research will help the management forecast future water shortages and make smarter water strategic choices.

Improved neural network with least square support vector machine for wastewater treatment process

This research offers a unique interval by using the predicting approach for discharge indicators of water quality data such as biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N). This is considered one of the significant quality metrics in wastewater treatment plants for water quality management as well as surveillance. To begin, the effluent information for BOD/NH3-N and their supplementary parameters are gathered. Hence BOD and NH3 are considered major feature sources for estimating water pollutants. BOD is high then oxygen level is very low in the water due to pollutants or algae. Ammonia nitrogen is an organic waste component in water from sewage. The significant characteristics with good correlation levels of BOD and NH3-N are examined and identified using a grey correlation analysis method after certain basic data pre-processing procedures. The BOD/NH3-N effluent information of a water treatment plant is predicted using an upgraded feed-forward neural network with the least square support vector machine (FFNN-LSSVM) method. An optimization approach for an enhanced feed-forward neural network (IFFNN) is built by Machine Learning Algorithms. The IFFNN used regular influent water quality, influent rate of flow, and Wastewater performance monitoring and operational conditions as input parameters. For future prediction, input variables were previous different wastewater quality measurements. Lastly, the analysis shows that, when compared to other current algorithms, the proposed methodology can forecast wastewater quality of water with high accuracy in predicting BOD and NH3 levels, limited computation duration, mean error less than 10% and R² is 90% proves better than existing techniques.

Water-Secure River Basins: A Compromise of Policy, Governance and Management with the Environment

Water-secure basins are a lifesaving goal of society that can be accomplished if political and water authorities, stakeholders, and the general public are networked and committed to effectively improve water security, river basin management, and water resource policies and governance [...]

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

The fourth industrial era, known as ‘Industry 4.0’ (I4.0), aided and abetted by the digital revolution, has attracted increasing attention among scholars and practitioners in the last decade. The adoption of I4.0 principles in Disaster Risk Management (DRM) research and associated industry practices is particularly notable, although its origins, impacts and potential are not well understood. In response to this knowledge gap, this paper conducts a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in DRM. The systematic literature review identified 144 relevant articles and then employed descriptive and content analysis of a focused set of 70 articles published between 2011 and 2021. The results of this review trace the growing trend for adoption of I4.0 tools and techniques in disaster management, and in parallel their influence in resilient infrastructure and digital construction fields. The results are used to identify six dominant clusters of research activity: big data analytics, Internet of Things, prefabrication and modularization, robotics and cyber-physical systems. The research in each cluster is then mapped to the priorities of the Sendai framework for DRR, highlighting the ways it can support this international agenda. Finally, this paper identifies gaps within the literature and discusses possible future research directions for the combination of I4.0 and DRM.