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Multi-Criteria Decision Making for Water Environment Management: Innovative Models and Applications

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 4360

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Special Issue Editors

Dr. Ahmed A. Abd El-Latif

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Guest Editor

Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 6131567, Egypt
Interests: post-quantum cryptography; cybersecurity; artificial intelligence of things; AI-based image processing; future networks; information hiding
Special Issues, Collections and Topics in MDPI journals

Dr. Edmond Shu-lim Ho

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Guest Editor

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: computer graphics; computer vision; robotics; motion analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Jialiang Peng

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Guest Editor

School of Data Science and Technology, Heilongjiang University, Harbin, China
Interests: biometric recognition; data management; cyberspace security

Special Issue Information

Dear Colleagues,

In recent years, scarcity of food and water resources have been quite dominant in certain economies. Also, in the upcoming years with the advent of many industries and production lines, the need to manage water resources optimally and effectively lies not just in the hands of policymakers and the government. It also is a responsibility of technology and participation at all levels. A collaboration of technological solutions, regulations, and policies must be driven towards a multi-criteria decision-making framework related to managing the water shortage risks, demand projection, optimization of distribution networks etc. Multi-criteria decision making (MCDM) has been used for optimizing land allocation, water use allocation, energy production, and environmental protection models over the years. Also, this type of analysis includes both the quantitative and the qualitative aspects into consideration. The major category of MCDM mathematical modelling is multi-attribute decision making (MADM) and multiple-objective decision making (MODM). The problems associated with current models include variation in various water management alternatives, context-dependent data resources, high uncertainty etc.

In order to overcome these limitations, certain water quality management related decision support framework integrates alternative management models with uncertain possibilities and consequence using AI-based algorithms. This is accomplished by scenario planning strategies which forecast certain aspects in water management planning in order to account for uncertain future impacts in the model. Certain models use multi-attribute value theory to obtain suitable results and preferences based on the input values and objectives. Such models work based on a value function. Also, such decision support systems do not work solely based on the subjective preferences of the users related to water management but on the available knowledge resources and big data. Also, innovative technologies and models integrate the multi-sensor remote sensing and earth observation data for helping such multi-criteria decision-making systems and solutions. But the common process involved in such a framework for water management involves the following steps: definition of the decision support context and scope; identification of objectives and preferences as input data; structuring the objectives in a hierarchical format; selection and assessment of alternatives; formulation of a value function; and prediction of possible risks and consequences. In spite of the common framework and models involved, it is important to develop a large set of models and value functions that are suitable to cater to the needs of a diverse range of problems and needs in water management systems.

Dr. Ahmed A. Abd El-Latif
Dr. Edmond Shu-lim Ho
Dr. Jialiang Peng
Guest Editors

Manuscript Submission Information

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Keywords

integrated groundwater and stormwater management system using MCDM models for an IT-based company
collaboration of water level sensors and water quality management devices for defining the framework of an MCDM-based water management system
a comprehensive assessment of perennial rivers in India using a multi-criteria decision-making model
development of an MCDM-based water management system to control eutrophication and water pollution
integration of big data analytics for defining and determining a knowledge framework for impact assessment in water quality control and distribution
optimization of measurable multi attribute value functions for performing MCDM operations in water management systems
integrated assessment and modelling of freshwater and wastewater management systems
design and development of an integrated catchment management system for mitigating quality issues in water using the MCDM approach
use of Artificial Intelligence (AI) for developing real-time modelling for allocation of water resources in sustainable and smart cities
development of an evaluation and planning system for water management in urban cities

Published Papers (2 papers)

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Research

16 pages, 6896 KiB

Open AccessArticle

Detection of Background Water Leaks Using a High-Resolution Dyadic Transform

by Eduardo Trutié-Carrero, Diego Seuret-Jiménez, José M. Nieto-Jalil, Julio C. Herrera-Díaz, Jorge Cantó and J. Jesús Escobedo-Alatorre

Water 2023, 15(4), 736; https://doi.org/10.3390/w15040736 - 13 Feb 2023

Cited by 2 | Viewed by 2016

Abstract

This article solves the problem of detecting water leaks with a minimum size of down to

1 mm

in diameter. Two new mathematical tools are used to solve this problem: the first one is the

T_{e}

cross-spectral density and the second [...] Read more.

This article solves the problem of detecting water leaks with a minimum size of down to

1 mm

in diameter. Two new mathematical tools are used to solve this problem: the first one is the

T_{e}

cross-spectral density and the second is

T_{e}

coherence. These mathematical tools provide the possibility of discriminating spurious frequency components, making use of the property of multi-sensitivity. This advantage makes it possible to maximize the sensitivity of the frequency spectrum. The wavelet function used was Daubechies

45

, because it provides an attenuation of

150 dB

in the rejection band. The tools were validated with two scenarios. For the first scenario, a synthetic signal was analyzed. In the second scenario, two types of background leakage were analyzed: the first one has a diameter of

1 mm

with a signal-to-noise ratio of

2.82 dB

and flow rate of

33.7 mL / s

, and the second one has a diameter of

4 mm

with a signal-to-noise ratio of

9.73 dB

with a flow rate of

125.0 mL / s

. The results reported in this paper show that both the

T_{e}

cross-spectral density and

T_{e}

coherence are higher than those reported in scientific literature. Full article

(This article belongs to the Special Issue Multi-Criteria Decision Making for Water Environment Management: Innovative Models and Applications)

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16 pages, 9443 KiB

Open AccessArticle

Optimal Choices in Decision Supporting System for Network Reservoir Operation

by Rapeepat Techarungruengsakul, Ratsuda Ngamsert, Teerawat Thongwan, Rattana Hormwichian, Kittiwet Kuntiyawichai, Seyed Mohammad Ashrafi and Anongrit Kangrang

Water 2022, 14(24), 4090; https://doi.org/10.3390/w14244090 - 14 Dec 2022

Cited by 5 | Viewed by 1911

Abstract

The aim of this research was to identify optimal choices in decision support systems for network reservoirs by using optimal rule curves under four scenarios related to water scarcity and overflow situations. These scenarios were normal water shortage, high water shortage, normal overflow and high overflow situations. The application of various optimization techniques, including Harris Hawks Optimization (HHO), Genetic Algorithm (GA), Wind-Driven Optimization (WDO) and the Marine Predator Algorithm (MPA), in conjunction with a reservoir simulation model, was conducted to produce alternative choices, leading to suitable decision-making options. The Bhumibol and Sirikit reservoirs, situated in Thailand, were selected as the case study for the network reservoir system. The objective functions for the search procedure were the minimal average water shortage per year, the minimal maximum water shortage and the minimal average water spill per year in relation to the main purpose of the reservoir system using the release criteria of the standard operating policy (SOP) and the hedging rule (HR). The best options of each scenario were chosen from 152 options of feasible solutions. The obtained results from the assessment of the effectiveness of alternative choices showed that the best option for normal water scarcity was the rule curve with the objective function of minimal average water shortage per year, using HR and recommended SOP for operation, whereas the best option for high-water shortage situation was the rule curves with objective function of minimal of maximum water shortage using HR and recommended HR for operation. For overflow situation, the best option for normal overflow situation was the rule curves with objective function of minimal average water spill per year using HR and the recommended SOP for operation, whereas the best option for the high overflow situation was the rule curve with the objective function of minimal average water spill per year using HR and the recommended HR for operation. When using the best curves according to the situation, this would result in a minimum water shortage of 153.789 MCM/year, the lowest maximum water shortage of 1338.00 MCM/year, minimum overflow of 978.404 MCM/year and the lowest maximum overflow of 7214.00 MCM/year. Finally, the obtained findings from this study would offer reliability and resiliency information for decision making in reservoir operation for the multi-reservoir system in the upper region of Thailand. Full article

(This article belongs to the Special Issue Multi-Criteria Decision Making for Water Environment Management: Innovative Models and Applications)

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