Application of NSGA-II and Improved Risk Decision Method for Integrated Water Resources Management of Malian River Basin

Abstract

The Malian River Basin is the Longdong grain elevator and a new oil and energy base of East Gansu Province. Limited water resources programming utilization is a key for the development of the socio-economic and energy industry, as well as the improvement of the ecological environment. An analytical framework for assessing socioeconomic development, rational allocation of water resources, and guiding policy development is proposed in this study. A decision tree method was used in the risk analysis and was improved by introducing the expert advisory probabilistic method into the sensitivity analysis to reduce cognitive bias. A large-system multi-objective model was developed to solve the problem of the rational allocation of available water resources and for benefit maximization among water users. The Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-II) method was used to generate a solution. The water supply amount within the basin was 8.69 × 108 m3 and the water shortage rate was 15.90%. The optimization model method had better distribution results than the weights method without new water supply. Through the model method results, the water saving potential was found and the related policies were proposed. The framework and methods can further provide a reference for both the planning of water resources and the formulation of regulatory policies and will greatly alleviate water crises in semi-arid areas.

Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents

“…NSGA-II is the standard metaheuristic for solving multi-objective optimization problems [29]. It has been applied to various search and optimization problems, such as scheduling problems [45], resource allocation problems [19], and optimal parameter selection problems [39]. The algorithm modifies the processes of mating and survival selection of individuals.…”

Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization

“…Evaluating different weight combinations would require extensive analysis which would change the focus of this research. Therefore, here presented multi-objective optimization problem is solved using the Pareto-based optimization algorithm, Nondominated Sorting Genetic Algorithm (NSGA-II; Deb et al 2002), which is widely used for solving multi-objective optimization tasks for water resources (Artina et al 2012;Darvishi & Kordestani 2019;Gao et al 2019;Wang et al 2019;Gaur et al 2021). Here, this algorithm is used to improve the initial tuning solution provided by the manual procedure.…”

A New Multi-Objective Optimization Model of Water Resources Considering Fairness and Water Shortage Risk

“…Because of their ability to perform large-scale and complex calculations and because they have the advantage of high versatility, they have been widely used to obtain solutions of water resource optimization models. Such methods include the genetic algorithm [14], particle swarm algorithm [15], non-dominated sorting genetic algorithm [16], and their modified versions [17]. The most popular among them is an evolutionary algorithm using a reference-point-based non-dominated sorting approach (NSGA-III) [18], because it has better convergence and strong practicability when dealing with three or more objectives.…”