To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
(K0.47Na0.47Li0.06)NbO3 (KNLN) lead-free ceramics were prepared by molten salt synthesis (MSS) method using k2CO3-Na2CO3 eutectic mixtures as the flux. The microstructure and piezoelectric properties when sintered at ...(K0.47Na0.47Li0.06)NbO3 (KNLN) lead-free ceramics were prepared by molten salt synthesis (MSS) method using k2CO3-Na2CO3 eutectic mixtures as the flux. The microstructure and piezoelectric properties when sintered at 980-1 030 ℃ were investigated. The calcined powders were examined by X-ray diffraction. The microstructure of the calcined powders and sintered bodies was observed using a scanning electron microscope (SEM).The piezoelectric constant d33 was measured using a quasi-static piezoelectric d33 meter. The planar coupling coefficient Kp was calculated by resonance-antiresonance method. The experimental data for each sample's performance indicators were the average values of 8 specimens. The relative densities of sintered specimens are above 97%, and the dielectric loss is below 0.03. It was found that (K0.47Na0.47Li0.06)NbO3 prepared by MSS is compact and lead-free. The piezoelectric constant d33 is 216 pC·N-1 and the planar electromechanical coupling factor Kp is 0.352.展开更多
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
基金Supported by National Natural Science Foundation of China (No.10232030)Key Laboratory for Advanced Ceramics and Machining Technology,Ministry of Education,Tianjin University (No. x06050)
文摘(K0.47Na0.47Li0.06)NbO3 (KNLN) lead-free ceramics were prepared by molten salt synthesis (MSS) method using k2CO3-Na2CO3 eutectic mixtures as the flux. The microstructure and piezoelectric properties when sintered at 980-1 030 ℃ were investigated. The calcined powders were examined by X-ray diffraction. The microstructure of the calcined powders and sintered bodies was observed using a scanning electron microscope (SEM).The piezoelectric constant d33 was measured using a quasi-static piezoelectric d33 meter. The planar coupling coefficient Kp was calculated by resonance-antiresonance method. The experimental data for each sample's performance indicators were the average values of 8 specimens. The relative densities of sintered specimens are above 97%, and the dielectric loss is below 0.03. It was found that (K0.47Na0.47Li0.06)NbO3 prepared by MSS is compact and lead-free. The piezoelectric constant d33 is 216 pC·N-1 and the planar electromechanical coupling factor Kp is 0.352.
