Considering the fact that the original two-parameter LCM model can only be used to investigate rainfall losses during the runoff period because the initial abstraction is not included, the LCM model was redefined as a...Considering the fact that the original two-parameter LCM model can only be used to investigate rainfall losses during the runoff period because the initial abstraction is not included, the LCM model was redefined as a three-parameter model, including the initial abstraction coefficient l, the initial abstraction Ia, and the rainfall loss coefficient R. The improved LCM model is superior to the original two-parameter model, which only includes r and R, where r is the initial rainfall loss index and can be calculated with l using the Soil Conservation Service curve number （SCS-CN） method, with r = 1/（1 ＋ λ）. The trial method was used to determine the parameter values of the improved LCM model at the watershed scale for 15 flood events in the Hongde Basin in China. The results show that larger r values are associated with smaller R values, and the parameter R ranges widely from 0.5 to 2.0. In order to improve the practicability of the LCM model, r = 0.833 with λ = 0.2 is reasonable for simplifying calculation. When the LCM model is applied to arid and semi-arid regions, rainfall without yielding runoff should be deducted from the total rainfall for more accurate estimation of rainfall-runoff.展开更多
目前,文本分类的研究主要集中在通过优化文本分类器来增强分类性能。然而,标签和文本之间的联系并没有得到很好的利用。尽管BERT对文本特征的处理表现出了非常好的效果,但对文本和标签的特征提取还有一定的提升空间。文中通过结合标签...目前,文本分类的研究主要集中在通过优化文本分类器来增强分类性能。然而,标签和文本之间的联系并没有得到很好的利用。尽管BERT对文本特征的处理表现出了非常好的效果,但对文本和标签的特征提取还有一定的提升空间。文中通过结合标签混淆模型(Label Confusion Model,LCM),提出一种基于BERT和LCM的文本分类模型(Model Based on BERT and Label Confusion,BLC),对文本和标签的特征进一步做了处理。充分利用BERT每一层的句向量和最后一层的词向量,结合双向长短时记忆网络(Bi-LSTM)得到文本表示,来替代BERT原始的文本特征表示。标签在进入LCM之前,使用自注意力网络和Bi-LSTM提高标签之间相互依赖关系,从而提高最终的分类性能。在4个文本分类基准数据集上的实验结果证明了所提模型的有效性。展开更多
Runoff calculation is one of the key components in the hydrological modeling. For a certain spatial scale, runoff is a very complex nonlinear process. Currently, the runoff yield model in different hydrological models...Runoff calculation is one of the key components in the hydrological modeling. For a certain spatial scale, runoff is a very complex nonlinear process. Currently, the runoff yield model in different hydrological models is not unique. The Chinese LCM model and the American SCS model describe runoff at the macroscopic scale, taking into account the rela- tionship between total actual retention and total rainfall and having a certain similarity. In this study, by comparing the two runoff yield models using theoretical analyses and numerical simulations, we have found that： （1） the SCS model is a simple linear representation of the LCM model, and the LCM model reflects more significantly the nonlinearity of catchment runoff. （2） There are strict mathematical relationships between parameters （R, r） of the LCM model and between parameters （S） of the SCS model, respectively. Parameters （R, r） of the LCM can be determined using the research results of the SCS model parameters. （3） LCM model parameters （R, r） can be easily obtained by field experiments, while SCS parameters （S） are difficult to measure. Therefore, parameters （R, r） of the LCM model also can provide the foundation for the SCS model. （4） The SCS model has a linear relationship between the reciprocal of total actual retention and the reciprocal of total rainfall during runoff period. The one-order terms of a Taylor series expansion of the LCM model describe the same relation- ship, which is worth further study.展开更多
Land use monitoring occupies a very important place in the analysis of the dynamics of the earth system. It helps to understand the organization and helps to provide relevant elements for the establishment of diagnose...Land use monitoring occupies a very important place in the analysis of the dynamics of the earth system. It helps to understand the organization and helps to provide relevant elements for the establishment of diagnoses and the development of environmental forecasts. The objective of this study is to follow the evolution of the agricultural landscape in the department of Séguéla from 1988 to 2020 and to make a prediction for 2050, in order to manage the spaces reasonably. The methodology adopted is based on the one hand on the processing of satellite images for the analysis of land cover and on the other hand on predictive modeling (LCM model) by 2050. The results obtained show that the land use maps produced after processing the satellite images made it possible to highlight the dynamics of the agricultural landscape in this part of the Worodougou region. During the period 1988 to 2020, we witness an increase in the area of cultivated territory as well as a slight reduction in wooded savannas which are largely made up of perennial crops (cashew trees, cocoa trees, coffee trees, etc.). These two aforementioned classes have respective annual rates of change of 2.42% and −0.44%. A scenario modeling land cover changes in 2050 with an overall accuracy of 80.35% revealed a continued growth of crops and fallows to the detriment of natural forests and wooded savannas.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.41271048 and 41330529)
文摘Considering the fact that the original two-parameter LCM model can only be used to investigate rainfall losses during the runoff period because the initial abstraction is not included, the LCM model was redefined as a three-parameter model, including the initial abstraction coefficient l, the initial abstraction Ia, and the rainfall loss coefficient R. The improved LCM model is superior to the original two-parameter model, which only includes r and R, where r is the initial rainfall loss index and can be calculated with l using the Soil Conservation Service curve number （SCS-CN） method, with r = 1/（1 ＋ λ）. The trial method was used to determine the parameter values of the improved LCM model at the watershed scale for 15 flood events in the Hongde Basin in China. The results show that larger r values are associated with smaller R values, and the parameter R ranges widely from 0.5 to 2.0. In order to improve the practicability of the LCM model, r = 0.833 with λ = 0.2 is reasonable for simplifying calculation. When the LCM model is applied to arid and semi-arid regions, rainfall without yielding runoff should be deducted from the total rainfall for more accurate estimation of rainfall-runoff.
文摘目前,文本分类的研究主要集中在通过优化文本分类器来增强分类性能。然而,标签和文本之间的联系并没有得到很好的利用。尽管BERT对文本特征的处理表现出了非常好的效果,但对文本和标签的特征提取还有一定的提升空间。文中通过结合标签混淆模型(Label Confusion Model,LCM),提出一种基于BERT和LCM的文本分类模型(Model Based on BERT and Label Confusion,BLC),对文本和标签的特征进一步做了处理。充分利用BERT每一层的句向量和最后一层的词向量,结合双向长短时记忆网络(Bi-LSTM)得到文本表示,来替代BERT原始的文本特征表示。标签在进入LCM之前,使用自注意力网络和Bi-LSTM提高标签之间相互依赖关系,从而提高最终的分类性能。在4个文本分类基准数据集上的实验结果证明了所提模型的有效性。
基金National Natural Science Foundation of China, No.41271048 The Key Program of National Natural Science Foundation of China, No.41330529
文摘Runoff calculation is one of the key components in the hydrological modeling. For a certain spatial scale, runoff is a very complex nonlinear process. Currently, the runoff yield model in different hydrological models is not unique. The Chinese LCM model and the American SCS model describe runoff at the macroscopic scale, taking into account the rela- tionship between total actual retention and total rainfall and having a certain similarity. In this study, by comparing the two runoff yield models using theoretical analyses and numerical simulations, we have found that： （1） the SCS model is a simple linear representation of the LCM model, and the LCM model reflects more significantly the nonlinearity of catchment runoff. （2） There are strict mathematical relationships between parameters （R, r） of the LCM model and between parameters （S） of the SCS model, respectively. Parameters （R, r） of the LCM can be determined using the research results of the SCS model parameters. （3） LCM model parameters （R, r） can be easily obtained by field experiments, while SCS parameters （S） are difficult to measure. Therefore, parameters （R, r） of the LCM model also can provide the foundation for the SCS model. （4） The SCS model has a linear relationship between the reciprocal of total actual retention and the reciprocal of total rainfall during runoff period. The one-order terms of a Taylor series expansion of the LCM model describe the same relation- ship, which is worth further study.
文摘Land use monitoring occupies a very important place in the analysis of the dynamics of the earth system. It helps to understand the organization and helps to provide relevant elements for the establishment of diagnoses and the development of environmental forecasts. The objective of this study is to follow the evolution of the agricultural landscape in the department of Séguéla from 1988 to 2020 and to make a prediction for 2050, in order to manage the spaces reasonably. The methodology adopted is based on the one hand on the processing of satellite images for the analysis of land cover and on the other hand on predictive modeling (LCM model) by 2050. The results obtained show that the land use maps produced after processing the satellite images made it possible to highlight the dynamics of the agricultural landscape in this part of the Worodougou region. During the period 1988 to 2020, we witness an increase in the area of cultivated territory as well as a slight reduction in wooded savannas which are largely made up of perennial crops (cashew trees, cocoa trees, coffee trees, etc.). These two aforementioned classes have respective annual rates of change of 2.42% and −0.44%. A scenario modeling land cover changes in 2050 with an overall accuracy of 80.35% revealed a continued growth of crops and fallows to the detriment of natural forests and wooded savannas.
