Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

Dong, Yuqi; Wang, Jianzhou; Niu, Xinsong; Zeng, Bo

doi:10.1002/for.2905

Cited by 12 publications

(12 citation statements)

References 45 publications

(49 reference statements)

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“…Furthermore, MLpowered technologies facilitate the development of smart cities (De Las Heras et al, 2020) and enhance the quality of life for residents (Cai et al, 2022;Khanna and Khanra, 2023). By harnessing the capabilities of ML, companies may address business problems while generating positive environmental and social outcomes (Dong et al, 2023;Wang et al, 2022aWang et al, , 2022bWang et al, , 2022c). An overview of previous similar review studies on the application of ML in sustainability is presented in Table 1.…”

Section: Machine Learningmentioning

confidence: 99%

“…Optimization of the performance is crucial in training ML models and finding optimal solutions (Dong et al, 2023;Li et al, 2022aLi et al, , 2022bLi et al, , 2022cLi et al, , 2022d. Naseri et al (2020) explored the best set of parameters for their objective function with a genetic algorithm (GA) and WCA.…”

Section: Leveraging Technology For a Greener Futurementioning

confidence: 99%

“…The land suitability assessment framework developed by Taghizadeh-Mehrjardi et al (2020) can be validated through testing in cases involving multiple crops. Dong et al (2023) propose a combined approach for water quality forecasting, which can be refined and optimized by improving the multi-objective optimization and data decomposition integration strategy. Integrating real-time data sources, advanced ML techniques and sensor technologies can enhance water pollution management systems in urban areas, enabling early warning systems, decision support tools and targeted interventions (Priyadarshini et al, 2022a(Priyadarshini et al, , 2022b.…”

Section: Model Establishmentmentioning

confidence: 99%

“…the eco-friendly concrete machine?Naseri et al (2020) How can machine learning be applied in designing a sustainable port with a special focus on carbon emission?Fahdi et al (2021) How to validate the existing land suitability assessment framework developed by testing the framework in cases of multiple crops Taghizadeh-Mehrjardi et al (2020)How to refine and optimizing the multi-objective optimization and data decomposition integration strategy to improve the accuracy and reliability of water quality predictions?Dong et al (2023) How to integrate ML methodology with other technologies such assensor technologies, real-time monitoring and advanced data analytics to develop comprehensive and adaptive systems for water pollution management…”

mentioning

confidence: 99%

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Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Section: Machine Learningmentioning

confidence: 99%

Section: Leveraging Technology For a Greener Futurementioning

confidence: 99%

Section: Model Establishmentmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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“…Based on the combined water quality prediction system of multi-objective optimization and improved data decomposition and integration strategy, Dong Yuqi realized real-time monitoring of the concentration and change trend of various pollutants under a single water pollution situation, and can automatically generate early warning information according to different environmental conditions [10]. Aghel B used the hybrid PSO neuro-fuzzy method to model and predict water quality parameters, and improved the global search ability by introducing adaptive weighting factors, thus realizing the dynamic planning of water quality data [11].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Evaluation Model Based on Principal Component and Particle Swarm Optimization Support Vector Machine

2021

WPPCP

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Water is an important natural resource and an essential material guarantee for human survival and development. With the continuous development and progress of social economy, people's demand for water resources is higher and higher, but the current water environment is not optimistic, so the water quality evaluation and management is very urgent. In practical work, in order to ensure that the water quality reaches the standard, a variety of methods need to be used for testing, the most common of which is water sample analysis. However, due to the immature testing technology, and most of the tasks of water sample determination are completed manually, many samples are difficult to obtain accurately, resulting in large deviation of the test results, which cannot meet the current water quality monitoring requirements. Principal component analysis, particle swarm optimization (PSO) and support vector machine (SVM) models are widely used in the research field of water pollutant content as effective prediction and analysis methods, which can well link various elements in the water environment and realize reasonable judgment and control of the impact laws and change laws of different substance concentration changes. At the same time, it plays a very positive role in protecting the ecological environment. In this paper, the principal component analysis model and PSO algorithm were described in detail. Combining the SVM model and the adaptive learning mechanism of PSO, a set of online water environment quality monitoring system based on PSO was designed and developed, which provided technical support for solving the problems of the lack of representativeness and poor accuracy of traditional water quality monitoring data. Compared with the traditional single neural network, the result showed that the SVM-based evaluation model had a higher prediction effect, and the accuracy was also improved by about 6.5%, which can reflect the water pollution situation more truly and reliably.

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The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

Weng,

Cheng,

Zhuang

et al. 2024

Journal of Forecasting

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This study analyzes the effect of governance quality (six aspects: government effectiveness; control of corruption; voice and accountability; regulatory quality; political stability and absence of violence; and rule of law) on the renewable and nonrenewable energy consumption prediction based on the SHapely Additive exPlanations method for model analysis and interpretability. The empirical findings indicate that the time‐varying contributions of six aspects of governance quality on nonrenewable (renewable) energy consumption predicting vary greatly in E‐7 and G‐7 countries. The time‐varying contribution of governance quality within countries is heterogeneous and asymmetrical, especially India (Germany) in E‐7 countries (G‐7 countries). The prediction contribution distribution of governance quality between countries is more discrete in G‐7 countries than E‐7 countries. Our results are of great importance to policymakers and investors for enhancing the renewable energy consumption level in overcoming environmental challenges based on the country itself through governance quality.

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Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

Cited by 12 publications

References 45 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

Water Quality Evaluation Model Based on Principal Component and Particle Swarm Optimization Support Vector Machine

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

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