Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Yang, Zhicheng; Li, Bo; Huang, Wu; Li, MengHua; Fan, Jun; Chen, Mengyu; Long, Jie

doi:10.1007/s11356-022-24604-2

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Precipitation was shown to be the principal growth factor impacting grain output and to have a suppressive effect on grain production when the effects of temperature and precipitation on the TFP in agriculture were compared, which was in line with the findings of Yang et al [54]. The data analysis results revealed that, in contrast to the findings of other studies, the rise in temperature was accompanied by an increase in the agricultural TFP.…”

Section: Limitations and Implicationssupporting

confidence: 86%

Climate Change, Farm Irrigation Facilities, and Agriculture Total Factor Productivity: Evidence from China

Liu

2023

Sustainability

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Due to the trend of global warming, individuals from all walks of life have paid close attention to how climate change affects food security. China is a sizable nation with a rich climate and a diverse range of food crops that are of interest to researchers. Additionally, there is little mention of agricultural technology and farm irrigation facilities in academic research on climate change and agricultural economic growth in China. As a result, this study uses the SBM model, panel fixed effect model, and SYS-GMM model to examine the development trend of climate change and food security based on the panel data of Chinese provinces from 2000 to 2020. The study found that China has maintained an average annual growth rate of 4.3% in agricultural total factor productivity (TFP) in recent years, despite the impact of extreme weather. The average annual precipitation has a depressing influence on the TFP in agriculture, while the average annual temperature has the opposite effect. The farm irrigation facilities and agricultural technology’s moderating impact is mostly shown in how well they attenuate the impact of climate change on the TFP in agriculture. Food crops have thereby improved their ability to survive natural risks and attain higher yields as a result of advancements in agricultural technology and increasing investment in contemporary farm irrigation facilities. The study’s conclusions are used in the article to make the suggestion that strengthening climate change adaptation is necessary to ensure food security. The strategic policy of “storing grain in technology and storing grain in the soil” and the advancement of contemporary agricultural technology must be put into reality while the management system for grain reserves is being improved.

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Section: Limitations and Implicationssupporting

confidence: 86%

Climate Change, Farm Irrigation Facilities, and Agriculture Total Factor Productivity: Evidence from China

Liu

2023

Sustainability

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“…The optimal water allocation amount obtained by the WAINM for each water use sector is determined by the water price, the unit loss of the penalty for exceeding water demand, the unit loss of opportunity loss for not meeting the water demand of the water use sector, and the CDFs of water use and supply. The water price and the unit loss of the penalty and opportunity loss can be determined by the market or by the government; It is difficult to predict regional water demand, because the influencing factors are complex and dynamic, and water demand prediction is full of challenges with greater uncertainty 48 , 49 . In the current study, considering the short and stable time series of industrial water demand in the study area, meanwhile, the number of newspapers the newsvendor purchased every day in the Newsvendor model was assumed to conform to a uniform distribution 35 – 37 , the demand for newspapers is uncertain, just like the uncertainty in the water demand of each water use sector in the water resources allocation model, therefore, the uniform distribution was chosen to express the uncertainties in water demand.…”

Section: Resultsmentioning

confidence: 99%

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

He,

Zheng,

Chen

et al. 2023

Sci Rep

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A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

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“…Ref. [22] used BP neural networks for carbon emission prediction at the macro-field with significant results, which indicates that neural networks also have very good performance in macro-field prediction and can replace statistical-based methods. As the demand for prediction accuracy increases, a single model cannot meet the demand, so more and more scholars study the combination of multiple models for prediction to improve accuracy.…”

Section: Prediction Methods Based On Bp Neural Networkmentioning

confidence: 99%

“…Machine Dominance Limitations [22] Multilayer feedforward neural network trained by error backpropagation algorithm With high prediction accuracy and the ability to learn nonlinear relationships between data, the model is more interpretable.…”

Section: Documentmentioning

confidence: 99%

Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison

Feng,

Chen,

et al. 2024

Energies

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The greenhouse effect formed by the massive emission of carbon dioxide has caused serious harm to the Earth’s environment, in which the power sector constitutes one of the primary contributors to global greenhouse gas emissions. Reducing carbon emissions from electricity plays a pivotal role in minimizing greenhouse gas emissions and mitigating the ecological, economic, and social impacts of climate change, while carbon emission prediction provides a valuable point of reference for the formulation of policies to reduce carbon emissions from electricity. The article provides a detailed review of research results on deep learning-based carbon emission prediction. Firstly, the main neural networks applied in the domain of carbon emission forecasting at home and abroad, as well as the models combining other methods and neural networks, are introduced, and the main roles of different methods, when combined with neural networks, are discussed. Secondly, neural networks were used to predict electricity carbon emissions, and the performance of different models on carbon emissions was compared. Finally, the application of neural networks in the realm of the prediction of carbon emissions is summarized, and future research directions are discussed. The article provides a reference for researchers to understand the research dynamics and development trend of deep learning in the realm of electricity carbon emission forecasting.

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Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province

Cited by 10 publications

References 33 publications

Climate Change, Farm Irrigation Facilities, and Agriculture Total Factor Productivity: Evidence from China

Climate Change, Farm Irrigation Facilities, and Agriculture Total Factor Productivity: Evidence from China

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison

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