Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines

Jalili, Ahmad; Najarchi, Mohsen; Shabanlou, Saeid; Jafarinia, Reza

doi:10.1007/s11356-022-22723-4

Cited by 17 publications

(2 citation statements)

References 23 publications

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“…The strategy used the NSGA-II and the WEAP simulator model, and introduced the support vector machine into the model. Experiments showed that the average error rate of the rule obtained by this method was less than 2.5% [12]. BP neural networks have been widely used in many fields because of their strong flexibility, fault tolerance and adaptability.…”

Section: Related Workmentioning

confidence: 91%

Logistics Path Planning Method using NSGA-II Algorithm and BP Neural Network in the Era of Logistics 4.0

2024

ijacsa

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The distribution of fresh food is affected by its perishable characteristics, and compared with ordinary logistics distribution, the distribution path needs to be very reasonably planned. However, the complexity of the actual road network and the time variation of traffic conditions are not considered in the existing food logistics planning methods. In order to solve this problem, this study takes road section travel prediction as the starting point, and uses the non-dominant ranking genetic algorithm II and the backpropagation network to construct a new logistics path planning model. Firstly, the road condition information detected by the retainer detection and the floating vehicle technology is integrated, and the travel time prediction is input into the backpropagation network model. In order to make the prediction model perform better, it is improved using a whale optimization algorithm. Then, according to the prediction results, the non-dominant ranking genetic algorithm II is used for distribution route planning. Through experimental analysis, the average distribution cost of method designed by this study was 9476 yuan, and the average carbon emission was 2871kg. Compared with the other three algorithms, the distribution cost was more than 15% lower, and the carbon emission was more than 12.5% lower. The planning method designed by the institute can achieve more reasonable, low-cost, and environmentally friendly logistics and distribution, and bring more satisfactory services to the lives of urban residents.

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Section: Related Workmentioning

confidence: 91%

Logistics Path Planning Method using NSGA-II Algorithm and BP Neural Network in the Era of Logistics 4.0

2024

ijacsa

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“…Data-driven frameworks, including machine-learning (ML) models, have emerged as a prominent focus and a topical subject in various engineering disciplines, notably in the realm of water and environmental engineering (Solomatine and Ostfeld, 2008;Giustolisi and Savic, 2009;Araghinejad, 2013). Whether it involves a more efficient optimization algorithm (e.g., Jalili et al, 2023;Wu et al, 2023), employing meticulous data mining methods (e.g., Aslam et al, 2022;Beig Zali et al, 2023;Zolghadr-Asli et al, 2023), developing sophisticated ML models (e.g., Ray et al, 2023;Sun et al, 2023), or, more recently, utilizing large-language models such as ChatGPT (e.g., Foroumandi et al, 2023;Halloran et al, 2023), the core premise of this sub-discipline, often referred to as hydroinformatics within the domain of water and hydrology-related science, lies in the potential of computational intelligence (CI) and, possibly, artificial intelligence (AI) to reshape the future of this field (Makropoulos and Savić, 2019;Loucks, 2023). In essence, hydroinformatics can be viewed as a management philosophy enabled by (CI/AI) technology, and its primary objective is to establish a systematic approach to representing and comprehending the intricate and multidimensional phenomena prevalent in water management.…”

Section: Introductionmentioning

confidence: 99%

A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics

Zolghadr-Asli,

Ferdowsi,

Savić

2024

Camb. prisms Water

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Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the model-centric approach. These endeavors can certainly pave the way for more tailor-fitted models capable of producing accurate results. However, such a perspective often neglects a fundamental assumption of these models, which is the importance of reliability, correctness, and accessibility of the data used in constructing them. This challenge arises from the prevalent model-centric paradigm of thinking in the field. An alternative approach, however, would prioritize placing data at the focal point, focusing on systematically enhancing current datasets and devising frameworks to improve data collection schemes. This suggests a paradigm shift toward more data-centric thinking in water and environmental engineering. Practically, this shift is not without challenges and necessitates smarter data collection rather than an excessive one. Equally important is the ethical and accurate collection of data, making it available to everyone while safeguarding the rights of individuals and other legal entities involved in the process.

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A comprehensive survey on NSGA-II for multi-objective optimization and applications

Sun

Liu

et al. 2023

Artif Intell Rev

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Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines

Cited by 17 publications

References 23 publications

Logistics Path Planning Method using NSGA-II Algorithm and BP Neural Network in the Era of Logistics 4.0

Logistics Path Planning Method using NSGA-II Algorithm and BP Neural Network in the Era of Logistics 4.0

A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics

A comprehensive survey on NSGA-II for multi-objective optimization and applications

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