Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules

Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)modelling.However,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored.Adopting Xunwu County,China,as an example,the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions,after which different landslide inventory sample missing conditions are simulated by random sampling.It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%,20%,30%,40%and 50%,as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation.Then,five machine learning models,namely,Random Forest(RF),and Support Vector Machine(SVM),are used to perform LSP.Finally,the LSP results are evaluated to analyze the LSP uncertainties under various conditions.In addition,this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions.Results show that(1)randomly missing landslide inventory samples at certain proportions(10%–50%)may affect the LSP results for local areas.(2)Aggregation of missing landslide inventory samples may cause significant biases in LSP,particularly in areas where samples are missing.(3)When 50%of landslide samples are missing(either randomly or aggregated),the changes in the decision basis of the RF model are mainly manifested in two aspects:first,the importance ranking of environmental factors slightly differs;second,in regard to LSP modelling in the same test grid unit,the weights of individual model factors may drastically vary.

An Isoindigo-Based ＂Double-Cable＂ Conjugated Polymer for SingleComponent Polymer Solar Cells

An isoindigo-based ＂double-cable＂ conjugated polymer bearing perylene bisimide side units was developed via Stille polymerization for application in single-component polymer solar cells, in which a power conversion efficiency of 1% with broad photo-response from 300 nm to 800 nm was achieved. There is no evidence of large phase separation confirmed by AFM images and photoluminescence （PL） spectra. The space charge limit current measurements and light intensity dependence measurements indicate that the low electron mobility and the significant recombination of pho- togenerated charge carriers in active layer mainly account for the low performance of our solar cells. Our results suggest that these ＂double-cable＂ are oromising candidates for use in single-component polymer solar cells with NIR photoresponse.

Ethynyl-linked perylene bisimide based electron acceptors for non-fullerene organic solar cells

In this work, star-shaped perylene bisimide（PBI） derivatives with spiro-aromatic cores linked with ethynyl units were developed as electron acceptors for non-fullerene organic solar cells. The ethynyl linkers were found to enhance the planarity of the conjugated backbone, resulting in high electron mobilities and near-infrared absorption. The ethynyl-linked PBI acceptors showed high power conversion efficiencies（PCEs） up to 4.27% due to the high short-circuit current density（Jsc） of 8.52 mA/cm^2 and fill factor（FF） of 0.59, while the PBI acceptor without ethynyl units provided a low PCE of 3.57% in nonfullerene solar cells. The results demonstrate that ethynyl units can be applied into designing new PBI electron acceptors with improved charge transport properties and photovoltaic performance.