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.

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.

Using unmanned aerial vehicle (UAV) hyperspectral images to accurately estimate the chlorophyll content of summer maize is of great significance for crop growth monitoring, fertilizer management, and the development of precision agriculture. Hyperspectral imaging data, analytical spectral devices (ASD) data, and SPAD values of summer maize in different key growth periods were obtained under the conditions of a micro-spray strip drip irrigation water supply. The hyperspectral data were preprocessed by spectral transformation methods. Then, several algorithms including Findpeaks (FD), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and CARS_SPA were used to extract the sensitive characteristic bands related to SPAD values from the hyperspectral image data obtained by UAV. Subsequently, four machine learning regression models including partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN) were used to establish SPAD value estimation models. The results showed that the correlation coefficient between the ASD and UAV hyperspectral data was greater than 0.96 indicating that UAV hyperspectral image data could be used to estimate maize growth information. The characteristic bands selected by different algorithms were slightly different. The CARS_SPA algorithm could effectively extract sensitive hyperspectral characteristics. This algorithm not only greatly reduced the number of hyperspectral characteristics but also improved the multiple collinearity problem. The low frequency information of SSR in spectral transformation could significantly improve the spectral estimation ability for SPAD values of summer maize. In the accuracy verification of PLSR, RF, XGBoost, and the DNN inversion model based on SSR and CARS_SPA, the determination coefficients (R²) were 0.81, 0.42, 0.65, and 0.82, respectively. The inversion accuracy based on the DNN model was better than the other models. Compared with high-frequency information, low-frequency information (DNN model based on SSR and CARS_SPA) had a strong estimating ability for SPAD values in summer maize canopy. This study provides a reference and technical support for the rapid non-destructive testing of summer maize.

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

Residents in industrial cities may be exposed to potentially toxic elements (PTEs) in soil that increase chronic disease risks. In this study, six types of PTEs (Zn, As, Cr, Ni, Cu, and Pb) in 112 surface soil samples from three land use types—industrial land, residential land, and farmland—in Tonghua City, Jilin Province were measured. The geological accumulation index and pollution load index were calculated to assess the pollution level of metal. Meanwhile, the potential ecological risk index, hazard index, and carcinogenic risk were calculated to assess the environmental risks. The spatial distribution map was determined by the ordinary kriging method, and the sources of PTEs were identified by factor analysis and cluster analysis. The average concentrations of Zn, As, Cr, Ni, Cu, and Pb were 266.57, 15.72, 72.41, 15.04, 20.52, and 16.30 mg/kg, respectively. The results of the geological accumulation index demonstrated the following: Zn pollution was present in all three land use types, As pollution in industrial land cannot be neglected, Cr pollution in farmland was higher than that in the other two land use types. The pollution load index decreased in the order of industrial land > farmland > residential land. Multivariate statistical analysis divided the six PTEs into three groups by source: Zn and As both originated from industrial activities; vehicle emissions were the main source of Pb; and Ni and Cu were derived from natural parent materials. Meanwhile, Cr was found to come from a mixture of artificial and natural sources. The soil environment in the study area faced ecological risk from moderate pollution levels mainly contributed by As. PTEs did not pose a non-carcinogenic risk to humans; however, residents of the three land use types all faced estimated carcinogenic risks caused by Cr, and As in industrial land also posed high estimated carcinogenic risk to human health. The conclusion of this article provides corresponding data support to the government’s policy formulation of remediating different types of land and preventing exposure and related environmental risks.

Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson’s correlation coefficient between prediction results and meteorological yield was 0.79 (p < 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost.