Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
Hydrogen peroxide(H_(2)O_(2))has been recognized as a rather important chemical with extensive applications in environmental protection,chemical synthesis,military manufacturing,etc.However,the mature industrial strat...Hydrogen peroxide(H_(2)O_(2))has been recognized as a rather important chemical with extensive applications in environmental protection,chemical synthesis,military manufacturing,etc.However,the mature industrial strategy(i.e.,anthraquinone oxidation)for H_(2)O_(2)production is featured with heavy pollution and high energy consumption.Photocatalytic technology has been regarded as a sustainable strategy to convert H_(2)O and O_(2)into H_(2)O_(2).Recently,porous organic framework materials(POFs)including metalorganic frameworks(MOFs),covalent organic frameworks(COFs),covalent triazine frameworks(CTFs),covalent heptazine frameworks(CHFs),and hydrogen-bonded organic frameworks(HOFs)have exhibited significant potential for green H_(2)O_(2)photosynthesis by virtue of their diverse synthesis methods,enormous specific surface areas,flexible design,adjustable band structure,and photoelectric property.In this review,the recent advances in H_(2)O_(2)photosynthesis based on POFs are comprehensively investigated.The modification strategies to improve H_(2)O_(2)production and their photocatalytic mechanisms are systematically analyzed.The current challenges and future perspectives in this field are highlighted as well.This review aims to give a complete picture of the research effort made to provide a deep understanding of the structure-activity relationship of H_(2)O_(2)photogeneration over POFs,thus inspiring some new ideas to tackle the challenges in this field,and finally stimulating the efficient development of organic semiconductors for sustainable photogeneration of H_(2)O_(2).展开更多
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported by the Yunnan Fundamental Re-search Project(No.202301AU070037)the Education Depart-ment Project of Yunnan Province(No.2023J0837)+5 种基金the Talent Introduction Project of Kunming University(Nos.YJL23018 and YJL23019)the Cooperative Research Program of Yunnan Provincial Undergraduate Universities’Association(No.202301BA070001-128)the National Natural Science Foundation of China(Nos.22062026 and22165016)the Industrialization Cultivation Project(No.2016CYH04)the Yunling Scholar(No.YNWR-YLXZ-2019-002)the Reserve Talents of Young and Middle-aged Academic and Tech-nical Leaders in Yunnan Province(No.202205AC160042)。
文摘Hydrogen peroxide(H_(2)O_(2))has been recognized as a rather important chemical with extensive applications in environmental protection,chemical synthesis,military manufacturing,etc.However,the mature industrial strategy(i.e.,anthraquinone oxidation)for H_(2)O_(2)production is featured with heavy pollution and high energy consumption.Photocatalytic technology has been regarded as a sustainable strategy to convert H_(2)O and O_(2)into H_(2)O_(2).Recently,porous organic framework materials(POFs)including metalorganic frameworks(MOFs),covalent organic frameworks(COFs),covalent triazine frameworks(CTFs),covalent heptazine frameworks(CHFs),and hydrogen-bonded organic frameworks(HOFs)have exhibited significant potential for green H_(2)O_(2)photosynthesis by virtue of their diverse synthesis methods,enormous specific surface areas,flexible design,adjustable band structure,and photoelectric property.In this review,the recent advances in H_(2)O_(2)photosynthesis based on POFs are comprehensively investigated.The modification strategies to improve H_(2)O_(2)production and their photocatalytic mechanisms are systematically analyzed.The current challenges and future perspectives in this field are highlighted as well.This review aims to give a complete picture of the research effort made to provide a deep understanding of the structure-activity relationship of H_(2)O_(2)photogeneration over POFs,thus inspiring some new ideas to tackle the challenges in this field,and finally stimulating the efficient development of organic semiconductors for sustainable photogeneration of H_(2)O_(2).
