Carbon Emission Prediction and Emission Reduction Analysis of Wastewater Treatment Plant Based on Machine Learning and Deep Learning DOI Creative Commons

Fangqin Liu,

Ning Ding,

Guanghua Zheng

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 27, 2024

Abstract Accurate accounting and prediction of carbon emissions from sewage treatment plants is the basis for exploring low-carbon measures to reduce pollution emissions. Although emission models have been widely used in construction, transportation other fields, research field wastewater still lacking, existing mostly limited a single link or energy consumption, which makes it difficult control whole plant as order realize reduction plant. This study proposes hybrid framework based on machine learning deep learning, integrates multiple algorithms has strong adaptability generalization ability. The pre-framework uses Pearson correlation coefficient select feature values, constructs combined model selected features using support vector (SVR) artificial neural network (ANN), optimizes parameters structure Gray Wolf Optimization (GWO) algorithm. results show that stronger performance compared with models, mean absolute percentage error (MAPE) 0.49% an R2 0.9926. In addition, this establishes six future development scenarios historical data trends policy outlines, provide recommendations plants. can reference efficient management achieving neutrality

Язык: Английский

Exploring the key influencing factors of low-carbon innovation from urban characteristics in China using interpretable machine learning DOI
Wentao Wang, Dezhi Li, Shenghua Zhou

и другие.

Environmental Impact Assessment Review, Год журнала: 2024, Номер 107, С. 107573 - 107573

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

14
Impact of war on the environment: ecocide DOI Creative Commons

Yohannes Desalegn Wirtu,

Umer Abdela

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Фев. 6, 2025

This paper reviews the militaristic consequences on Mother Earth and in particular ecocide or mass degradation of biological forms regards to war. Wars are recorded from ancient Egyptian dynasty modern era, all left concerning effects mother nature such as deforestation, loss biodiversity, soil erosion, water, air pollution. Some conflicts that especially noted Vietnam War, Gulf contemporary wars Ukraine which considered examples today’s context. To provide a clearer understanding our methodology, we employed comprehensive literature review approach. involved systematically analyzing existing studies document environmental impacts warfare across various historical conflicts. We categorized findings based specific consequences, biodiversity loss, Additionally, incorporated case significant illustrate patterns ecocide. The looks at how hazard is performed through direct destructive activities like bombings migration populations their needs socio-economic pursuits. Legal instruments those international level law developing crime also examined for problem ecological injustice. Finally, rehabilitation reconstruction measures including community-based efforts reforestation restoration ecosystems. finally ends by advocating threats calling cooperation treaties no post-war countries.

Язык: Английский

Процитировано

1
Evaluation of resource-based rural sewage treatment system driven by clean energy in Northwest China DOI

Zilan Liao,

Shangbin Ma,

Pengyu Li

и другие.

Journal of Water Process Engineering, Год журнала: 2025, Номер 70, С. 106932 - 106932

Опубликована: Янв. 9, 2025

Язык: Английский

Процитировано

0
Impact of renewable energy transition on aquatic ecosystems DOI Creative Commons

T. P. Baranovskaya,

V. N. Fursov

E3S Web of Conferences, Год журнала: 2025, Номер 614, С. 04020 - 04020

Опубликована: Янв. 1, 2025

The global transition to renewable energy sources is a critical component of efforts mitigate climate change and promote sustainable development. However, this shift has significant implications for aquatic ecosystems, which are integral biodiversity, water quality, ecosystem services. This paper examines the impact on ecosystems by analyzing various technologies, including hydroelectric power, wind energy, solar bioenergy. Through comprehensive bibliographic analysis, study explores both positive negative effects these technologies environments. Key findings indicate that while generally contribute reducing greenhouse gas emissions decreasing reliance fossil fuels, they can also lead habitat disruption, quality degradation, alterations in biodiversity. For instance, dams fragment habitats impede fish migration, turbines may affect marine life through noise pollution physical collisions. Conversely, farms offshore installations offer opportunities restoration creation artificial reefs. highlights regional case studies from North America, Europe, Asia illustrate diverse impacts mitigation strategies employed. Recommendations provided policymakers stakeholders balance development with preservation emphasizing need integrated planning, environmental assessments, implementation best practices. contributes ongoing discourse elucidating complex interactions between infrastructure environments, thereby informing future research policy formulation.

Язык: Английский

Процитировано

0
Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis DOI
Selin Yalçın, Ertuğrul Ayyıldız

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(38), С. 51000 - 51024

Опубликована: Авг. 6, 2024

Язык: Английский

Процитировано

1
Edge-Cloud Collaboration-Driven Predictive Planning Based on Lstm-Attention for Wastewater Treatment DOI
Shuaiyin Ma, Wei Ding,

Yujuan Zheng

и другие.

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

0
Carbon Emission Prediction and Emission Reduction Analysis of Wastewater Treatment Plant Based on Machine Learning and Deep Learning DOI Creative Commons

Fangqin Liu,

Ning Ding,

Guanghua Zheng

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 27, 2024

Abstract Accurate accounting and prediction of carbon emissions from sewage treatment plants is the basis for exploring low-carbon measures to reduce pollution emissions. Although emission models have been widely used in construction, transportation other fields, research field wastewater still lacking, existing mostly limited a single link or energy consumption, which makes it difficult control whole plant as order realize reduction plant. This study proposes hybrid framework based on machine learning deep learning, integrates multiple algorithms has strong adaptability generalization ability. The pre-framework uses Pearson correlation coefficient select feature values, constructs combined model selected features using support vector (SVR) artificial neural network (ANN), optimizes parameters structure Gray Wolf Optimization (GWO) algorithm. results show that stronger performance compared with models, mean absolute percentage error (MAPE) 0.49% an R2 0.9926. In addition, this establishes six future development scenarios historical data trends policy outlines, provide recommendations plants. can reference efficient management achieving neutrality

Язык: Английский

Процитировано

0