Search

The negative impact of high-temperature disaster on agricultural production is becoming more and more serious, and reducing the vulnerability to high-temperature disaster is fundamental to achieving sustainable agricultural development. This study is mainly focused on the vulnerability to agricultural high-temperature disaster in Shaanxi Province, China. Firstly, 15 indicators were selected from the perspectives of exposure, sensitivity, and adaptability. Secondly, the combined weighting method (Critic-G1 model) was used to determine the weight of each index. Based on the aforementioned procedures, the Kullback–Leibler (KL)-distance-improved TOPSIS model was utilized to evaluate the vulnerability. Lastly, the obstacle model was used to analyze the influencing factors and to make recommendations for disaster prevention and mitigation. The results show that: (1) The improved TOPSIS model was closer to the results of the synthetical index method. (2) The northern and southern area of Shaanxi is more vulnerable to high-temperature disaster, especially in Ankang and Tongchuan. Low values are distributed in the Guanzhong Plain. (3) Sensitivity is the biggest obstacle to reducing the vulnerability to high-temperature disaster. Among the influencing factors, the meteorological yield reduction coefficient of variation, multiple cropping index and per capita net income of rural residents of the obstacle are high. Decreasing sensitivity should be accompanied by increasing adaptability to improve regional disaster preparedness and mitigation. The results of this study can provide a basis for the development of agricultural high-temperature disaster mitigation and loss reduction strategies and provide new ideas for future research. [ABSTRACT FROM AUTHOR]

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. [ABSTRACT FROM AUTHOR]

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects. [ABSTRACT FROM AUTHOR]