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Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province. [ABSTRACT FROM AUTHOR]

To identify apple canker casual agents and evaluate their pathogenicity and virulence in apple production hubs including West Azarbaijan, Isfahan and Tehran provinces; samples were collected from symptomatic apple trees. Pathogenic isolates on the detached branches were identified as Cytospora cincta, Diplodia bulgarica, Neoscytalidium dimidiatum and Eutypa cf. lata. E. cf. lata was reported as a potential apple canker causal agent in Iran for the first time based on the pathogenicity test on the detached branches, whereas it caused no canker symptoms in apple trees until 6 months after inoculation. Currently, E. cf. lata seems to be adapted to a single city. C. cincta, D. bulgarica and N. dimidiatum caused canker symptoms in apple trees. "C. cincta" and also "C. cincta and N. dimidiatum" were the most widespread and aggressive apple canker species, respectively, associated with apple canker in Iran. Therefore, they are considered to be the main threat to apple production in Iran and should be carefully monitored. Disease progress curve, area under the disease progress curve and optimum temperatures were determined for mentioned species. It is concluded that the establishment of each species occurs in appropriate areas and times in terms of the optimum temperature for their growth. [ABSTRACT FROM AUTHOR]

To identify apple canker casual agents and evaluate their pathogenicity and virulence in apple production hubs including West Azarbaijan, Isfahan and Tehran provinces; samples were collected from symptomatic apple trees. Pathogenic isolates on the detached branches were identified as Cytospora cincta, Diplodia bulgarica, Neoscytalidium dimidiatum and Eutypa cf. lata. E. cf. lata was reported as a potential apple canker causal agent in Iran for the first time based on the pathogenicity test on the detached branches, whereas it caused no canker symptoms in apple trees until 6 months after inoculation. Currently, E. cf. lata seems to be adapted to a single city. C. cincta, D. bulgarica and N. dimidiatum caused canker symptoms in apple trees. "C. cincta" and also "C. cincta and N. dimidiatum" were the most widespread and aggressive apple canker species, respectively, associated with apple canker in Iran. Therefore, they are considered to be the main threat to apple production in Iran and should be carefully monitored. Disease progress curve, area under the disease progress curve and optimum temperatures were determined for mentioned species. It is concluded that the establishment of each species occurs in appropriate areas and times in terms of the optimum temperature for their growth. [ABSTRACT FROM AUTHOR]