Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-s...Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwestern Pacific Ocean as an example, we evaluated the impact of different weighting schemes on the HSI models based on sea surface temperature, gradient of sea surface temperature and sea surface height. We compared differences in predicted fishing effort and HSI values resulting from different weighting. The weighting for different habitat variables could greatly influence HSI modeling and should be carefully done based on their relative importance in influencing the resource spatial distribution. Weighting in a multi-factor HSI model should be further studied and optimization methods should be developed to improve forecasting squid spatial distributions.展开更多
Floor water inrush is one of the main types of coal mine water hazards.With the development of deep mining,the prediction and evaluation of floor water inrush is particularly significant.This paper proposes a variable...Floor water inrush is one of the main types of coal mine water hazards.With the development of deep mining,the prediction and evaluation of floor water inrush is particularly significant.This paper proposes a variable weight model,which combines a multi-factor interaction matrix(MFIM)and the technique for order performance by similarity to ideal solution(TOPSIS)to implement the risk assessment of floor water inrush in coal mines.Based on the MFIM,the interaction between seven evaluation indices,including the confined water pressure,water supply condition and aquifer water yield property,floor aquifuge thickness,fault water transmitting ability,fracture development degree,mining depth and thickness and their influence on floor water inrush were considered.After calculating the constant weights,the active degree evaluation was used to assign a variable weight to the indices.The values of the middle layer and final risk level were obtained by TOPSIS.The presented model was successfully applied in the 9901 working face in the Taoyang Mine and four additional coal mines and the results were highly consistent with the engineering situations.Compared with the existing nonlinear evaluation methods,the proposed model had advantages in terms of the weighting,principle explanation,and algorithm structure.展开更多
In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies ofte...In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.展开更多
基金supported by the National 863 project (2007AA092201 2007AA092202)+4 种基金National Development and Reform Commission Project (2060403)"Shu Guang" Project (08GG14) from Shanghai Municipal Education CommissionShanghai Leading Academic Discipline Project (Project S30702)supported by the National Distantwater Fisheries Engineering Research Center, and Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, ChinaYong Chen’s involvement in the project was supported by the Shanghai Dongfang Scholar Program
文摘Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwestern Pacific Ocean as an example, we evaluated the impact of different weighting schemes on the HSI models based on sea surface temperature, gradient of sea surface temperature and sea surface height. We compared differences in predicted fishing effort and HSI values resulting from different weighting. The weighting for different habitat variables could greatly influence HSI modeling and should be carefully done based on their relative importance in influencing the resource spatial distribution. Weighting in a multi-factor HSI model should be further studied and optimization methods should be developed to improve forecasting squid spatial distributions.
基金Projects(41877239,51379112,51422904,40902084,41772298)supported by the National Natural Science Foundation of ChinaProject(2019GSF111028)supported by the Key Technology Research and Development Program of Shandong Province,China+1 种基金Project(2018JC044)supported by the Fundamental Research Funds of Shandong University,ChinaProject(JQ201513)supported by the Natural Science Foundation of Shandong Province,China。
文摘Floor water inrush is one of the main types of coal mine water hazards.With the development of deep mining,the prediction and evaluation of floor water inrush is particularly significant.This paper proposes a variable weight model,which combines a multi-factor interaction matrix(MFIM)and the technique for order performance by similarity to ideal solution(TOPSIS)to implement the risk assessment of floor water inrush in coal mines.Based on the MFIM,the interaction between seven evaluation indices,including the confined water pressure,water supply condition and aquifer water yield property,floor aquifuge thickness,fault water transmitting ability,fracture development degree,mining depth and thickness and their influence on floor water inrush were considered.After calculating the constant weights,the active degree evaluation was used to assign a variable weight to the indices.The values of the middle layer and final risk level were obtained by TOPSIS.The presented model was successfully applied in the 9901 working face in the Taoyang Mine and four additional coal mines and the results were highly consistent with the engineering situations.Compared with the existing nonlinear evaluation methods,the proposed model had advantages in terms of the weighting,principle explanation,and algorithm structure.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62133008)the Key Research and Development Program of Shandong Province(No.2025CXPT076).
文摘In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.
