Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation.

Glacial Lake Evolution (1962–2018) and Outburst Susceptibility of Gurudongmar Lake Complex in the Tista Basin, Sikkim Himalaya (India)

The Sikkim Himalayan glaciers and glacial lakes are affected by climate change like other parts of the Himalayas. As a result of this climate variability in the Sikkim Himalaya, a detailed study of the Gurudongmar lake complex (GLC) evolution and outburst susceptibility assessment is required. Glacial lake volume estimation and lake outburst susceptibility assessment were carried out to reveal different characteristics for all four lakes (GL-1, GL-2, GL-3, and GL-4) from the lake complex. Each of these lakes has a moderate to very high potential to outburst. As the dam of GL-1 provides no retention capacity, there is a very high potential of a combined effect with the sudden failure of the moraine-dams of GL-2 or GL-3 located upstream. Temporal analysis of GLC using optical remote sensing data and in-field investigations revealed a rapidly increasing total lake area by ~74 ± 3%, with an expansion rate of +0.03 ± 0.002 km2 a−1 between 1962 and 2018 due to climate change and ongoing glacier retreat. The overall lake area expansion rates are dependent on climate-driven factors, and constantly increasing average air temperature is responsible for the enlargement of the lake areas. Simultaneously, changes in GLC expansion velocity are driven by changes in the total amount of precipitation. The deficit in precipitation probably triggered the initial higher rate from 1962 to 1988 during the winter and spring seasons. The post-1990s positive anomaly in precipitation might have reduced the rate of the glacial lake area expansion considerably.

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.

Susceptibility Assessment of Two Tomato Hybrids Against Tuta absoluta Infestation Under Greenhouse Conditions

Abdel-Razek, A.S., N.M. Abd El-Ghany, M.A. Gesraha, T.A. Elewa and A. Moussa. 2021. Susceptibility Assessment of Two Tomato Hybrids Against Tuta absoluta Infestation Under Greenhouse Conditions. Arab Journal of Plant Protection, 39(4): 317-322. https://doi.org/10.22268/AJPP-39.4.317322 Tuta absoluta is a major insect pest which attack tomato plant varieties in Egypt. Several control attempts were carried out to avoid major crop losses by heavy application of chemical insecticides. The aim of the present study is to assess the susceptibility of infestation of T. absoluta of two tomato varieties (Shifa and Savera F1 hybrids) under greenhouse conditions. The tomato varieties were planted in two plantation periods at the district of Kom Hamada, El-Nubaria province, El-Behira Governorate. The susceptibility tests were done by random counting of leaf samples for the presence of T. absoluta mines and larvae. Both tomato varieties showed almost the same T. absoluta infestation level. Moreover, yield assessment was carried out for the two plantation periods by taking the average fruit weight yield (Kg/acre). The tomato yield results showed that Savera F1 hybrid tomato had higher yield compared to Shifa F1 hybrid variety, but such difference was not significant, However, the yield difference of both vireties between the two planting dates was significant. Keywords: Tomato, Tuta absoluta, susceptibility, leaf-mine, larvae, yield.