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Risk Assessment In Water Resource Management And Conservation
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By Siva Rama Krishnan Siva Rama Krishnan Scilit Preprints.org Google Scholar 1, * , M. K. Nallakaruppan M. K. Nallakaruppan Scilit Preprints.org Google Scholar 1, Rajeswari Chengoden Rajeswari Chengoden Scilit Preprints.org Google Scholar 1, Preprints Google Scholar 1, Preprints Google Scholar. Scholar 1, M. Iyapparaja M. Iyapparaja Scilit Preprints.org Google Scholar 1, Jayakumar Sadhasivam Jayakumar Sadhasivam Scilit Preprints.org Google Scholar 1 and Sankaran Sethuraman Sankaran Sethuraman Scilit Preprints.org Google Scholar 2
The Need For Green And Atmospheric Water Governance
Received: September 6, 2022 / Revised: October 2, 2022 / Accepted: October 10, 2022 / Published: October 17, 2022
(This article is part of the special issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
Water management is one of the critical issues discussed in most international forums. Water harvesting and recycling are the key needs to meet the global impending needs of the prevailing water crisis. To achieve this, we need to place more emphasis on water management techniques applied across different application categories. Given the population density index, there is a need to implement smart water management mechanisms for efficient distribution, conservation and maintenance of water quality standards for various purposes. The prescribed work discusses some key applications needed for effective water management. These are the latest trends in water recycling, water distribution, rainwater harvesting and irrigation management using various artificial intelligence (AI) models. The data collected for these applications is unique and varies by type. Therefore, there is a need to use a model or algorithm that can be applied to provide solutions for all these applications. Artificial intelligence (AI) and deep learning (DL) techniques, together with the Internet of Things (IoT), can facilitate the design of an intelligent water management system for sustainable water use of natural resources. This work examines various water management techniques and the use of AI/DL along with the IoT network and case studies and statistical sampling analysis to develop an effective water management framework.
Chapter 4: Water
Internet of Things (IoT); deep learning (DL); Artificial Intelligence (AI); water distribution; water quality; wastewater management; Water conservation
Water management includes the tasks of conserving water resources, extracting water, planning the available net water resources and distributing them to consumers as needs-based as possible. This includes establishing policies and practices to complete tasks under fragmented controls. Traditional methods and practices proved inadequate to carry out these tasks effectively. Water management practices need to be fully considered to keep the water resource sustainable in the long term. Almost 97% of the water is salty and unsuitable for drinking. Pollution also affects the available water. Several sectors such as intensive agriculture , wastewater (UN-Water, 2011), mining, industrial production and untreated urban runoff are the main causes of water pollution. Water from different sources must be used in an efficient manner, which is not possible with traditional water management methods. The existing methods of water utilization are not so cost effective  and there is also a reluctance to use the latest information and communication technologies (ICT). Machine learning algorithms have the potential to exponentially expand the learning process with a specific goal. Standard algorithms would not exponentially delete to cover undiscovered patterns in the new data sets. Water management is required in areas such as agriculture, public utilities, industry, mining, hydropower generation, aquaculture and livestock. In agriculture, the main challenges relate to water access methods, efficient water use and sustainable water conservation and production practices. In India, industries are the second highest water users and one of the largest sources of pollution. These industries obtain water from groundwater or surface water. The choice depends on various factors such as: B. the availability of groundwater, the availability of surface water, the costs and the demand for fresh water from the city administration. The water demand of industry/factories/mining continues to grow parallel to increasing urbanization. At the same time, there is an increasing amount of wastewater disposal without appropriate treatment in natural sources, which in turn also results in pollution of uncontaminated water. Due to the lack of adequate water management policies, effective monitoring methods need to be developed to enable the industry to maintain a storage treatment plant (STP) and use the treated water for its purpose. The ongoing drought is also a major problem for the population in major cities. Managing water supply during water scarcity season is one of the demanding tasks of Metropolitan Water Board officials. This is the challenge that paved the way for the use of intelligent techniques. The water distribution infrastructure modeled by the intelligent algorithms supports the efficient distribution of a safe and sustainable water supply to the general public. The model, built using smart technologies, would recommend smart devices that use less water, limit household water use, and charge tariffs for water use. The quality of water is assessed based on three classes of characteristics: physical, biological and chemical. The quality indicators (pollutants) of water include chlorophyll, pH, dissolved oxygen, heavy metal content, chloride and lead. There are several researchers who use the location and elevation of water bodies as input for various machine learning approaches to predict pollution . The intelligent systems such as IoT, deep learning  and machine learning algorithms could be used for processes such as leak management, flow monitoring, overload, contamination and developing strategies to adjust water consumption (Figure 1). The aim of this paper is to bring to the fore compelling new opportunities for smart technologies that can address the biggest challenges in water management.
The remainder of the paper is organized as follows: Section 2 discusses the background of water management, including the techniques and applications leveraging IoT and AI. Section 3 discusses the statistical sampling analysis. Section 4 discusses various case studies on the use of smart techniques in water management. Section 5 highlights the challenges and future directions. Section 6 provides an insight into how this study can be useful for researchers working on the implementation of smart water management systems. Finally, we list AI methods and challenges in water management systems.
Pdf) Resilience In Environmental Risk And Impact Assessment: Concepts And Measurement
Measuring water quality is a crucial task for efficient water distribution in smart cities. Detection of pollutants in the water resource is one of the main tasks. There are various AI-based methods for wastewater treatment. Zhao et al.  studied various AI techniques related to the wastewater treatment process. The authors also discussed the applications of AI for wastewater management as well as the costs and logistics associated with the entire process. The authors found that Artificial Neural Network (ANN) and Federated Learning (FL) are the two main effective AI methods used in the wastewater treatment process. In a similar survey, Malviya et al.  discussed the main parameters to be measured in wastewater such as chemical oxygen demand (COD), pH values, biological oxygen demand (BOD), nitrogen, turbidity and sulfur using genetic algorithms (GA). The authors also emphasized that the traces of heavy metals and other wastewater can be determined by implementing ANN in combination with other AI methods, which can achieve 85-90% accuracy. In another work, Nouran et al.  highlighted the use of adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and feed-forward neural network (FFNN) to determine BOD and COD of Tabriz Wastewater Treatment Plant (WWTP). out. The author also implemented autoregressive integrated moving average (ARIMA) to predict the outflows and distinguish the ability of nonlinear and linear models in forecasting. The main cause of water
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