Jinchi Zhang

Bio: Jinchi Zhang is an academic researcher from Nanjing Forestry University. The author has contributed to research in topics: Soil water & Ecosystem. The author has an hindex of 16, co-authored 70 publications receiving 803 citations.

A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

Abstract: For the purpose of comparing susceptibility mapping methods in Mizunami City, Japan, the landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map. A total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve (AUC). The validation results indicated that the logistic regression model could provide the highest AUC value (0.865), and a relatively high percentage of landslide points fell in the high and very high landslide susceptibility classes in this study. Furthermore, the paper also suggested that the model performances would be increased if appropriate landslide points were used for the calculation.

Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

Abstract: Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. This study aimed to identify the optimal quantitative method for landslide susceptibility mapping in Mizunami City, Japan. Three mathematical methods, logistic regression (LR), bivariate statistical analysis (BS), and multivariate adaptive regression spline models (MARSplines), were used to create landslide-susceptibility maps by comparing the past landslide distribution and the conditioning factor thematic maps. A landslide inventory map with a total of 222 landslide locations was extracted from aerial photographs provided by NIED (National Research Institute for Earth Science and Disaster Prevention, Japan). Then, the landslide inventory was randomly divided into two datasets: 50% was used for training the models and the remaining 50% for validation purposes. The landslide inventory map provided by NIED and an area under the ROC curve were used to evaluate model performance. We found that the MARSpline method resulted in a better prediction rate (79%) when compared to LR (75%) and BS (77%). In addition, a higher percentage of landslide polygons were found in the high to very high classes using the MARSpline method. Therefore, we concluded that the MARSpline method was the most efficient method for landslide susceptibility mapping in this study area.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

Abstract: The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

Abstract: With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m -bonacci sequences to detect eavesdropping. Meanwhile, we encode m -bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.

Arbuscular mycorrhizal fungi

Abstract: The potential disease suppressiveness of arbuscular mycorrhizal (AM) fungi of various origins on Bipolaris sorokiniana in barley has been investigated. Firstly, a survey considering the occurrence of AM fungi in arable fields in Sweden were conducted with the aim to exploit site specific genetic resources in relation to disease suppressiveness. Arbuscular mycorrhizal fungi were present at all 45 sampling sites surveyed all over Sweden at densities ranging from 3 up to 44 spores per gram air dried soil. The highest spore density was found in a semi-natural grassland and the lowest were found in a cereal monoculture. The AM fungi were then multiplied in trap cultures in the greenhouse with the aim to use these for studying potential disease suppressiveness. Thus, the effects of the AM fungi trap cultures on the transmission of seed-borne B. sorokiniana in barley were investigated, using the trap culture inocula, but also including inocula consisting on spore mixtures. The arbuscular mycorrhizal fungi were able to suppress the transmission of B. sorokiniana in aerial parts of barley plants. The degree of suppression varied with the origin of the AM fungal trap cultures. The trap culture inoculum with the highest suppression of the B. sorokiniana transmission originated from an organically managed barley field with undersown ley. The two spore-inocula with the best suppression of the pathogen originated from fields with winter wheat and spring barley, respectively. Eventually, an in vitro method was developed for studying the effect of AM fungal colonisation of roots on the development of foliar diseases and the reaction of the actual host plant of the disease causing organism. Using the developed method, it was indicated that AM fungal colonisation of barley plant suppressed the development of leaf necroses due to B. sorokiniana. Further in vitro studies on the interaction between B. sorokiniana and arbuscular mycorrhizal fungi showed that B. sorokiniana decrease the germination of the AM fungal spores. In conclusion, AM fungi suppress the development of B. sorokiniana in barley. My data suggest that for biocontrol of B. sorokiniana AM fungi should be considered.

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Abstract: Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope ...

Opportunities for biodiversity gains under the world/'s largest reforestation programme

Abstract: Reforestation is a critical means of addressing the environmental and social problems of deforestation. China’s Grain-for-Green Program (GFGP) is the world’s largest reforestation scheme. Here we provide the first nationwide assessment of the tree composition of GFGP forests and the first combined ecological and economic study aimed at understanding GFGP’s biodiversity implications. Across China, GFGP forests are overwhelmingly monocultures or compositionally simple mixed forests. Focusing on birds and bees in Sichuan Province, we find that GFGP reforestation results in modest gains (via mixed forest) and losses (via monocultures) of bird diversity, along with major losses of bee diversity. Moreover, all current modes of GFGP reforestation fall short of restoring biodiversity to levels approximating native forests. However, even within existing modes of reforestation, GFGP can achieve greater biodiversity gains by promoting mixed forests over monocultures; doing so is unlikely to entail major opportunity costs or pose unforeseen economic risks to households. China’s Grain for Green Program is the world’s largest reforestation program, encompassing tens of millions of hectares since 1999. Here, Hua et al. show that the majority of areas have been reforested with tree monocultures, but that planting mixed forests could increase animal biodiversity without imposing additional economic costs.