Fuzzy Neuro Approach to Water Management Systems
Abstract
This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes using two mostly widely used particular types of data driven models, namely recurrent neural networks (RNN) and fuzzy logic-based models.. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using RNN to find optimum reservoir level and predicting peak daily demands.
Read the paper
Related studies
- Green Chemistry: A Framework for a Sustainable Future
- Nano/micro-plastic, an invisible threat getting into the brain
- Emerging contaminants antibiotic resistance genes and microplastics in the environment: Introduction to 21 review articles published in CREST during 2018–2022
- Water Resources and Sustainable Development
- Nano- and microplastics in the workplace
- Water Enclosure and World-Literature: New Perspectives on Hydro-Power and World-Ecology
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.