Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

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Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

Geohazards are one of the most critical disasters that cause serious damage to engineering construction activities and peoples' lives and property; therefore, susceptibility evaluation of the geohazards is vital for planning future sustainable development and construction activities. This paper presents a multidisciplinary approach to divide the susceptibility grades of geohazards in Xingye County, using back‐propagation neural networks (BPNN), Geographical Information Systems, and grid analysis technology on remote sensing data. Twelve geohazards related causal factors were selected to reflect the physical geography, basic geology, ecological environment, human activities, and other characteristics of Xingye; the factors are: slope, aspect, topographic curvature, precipitation, normalized differential vegetation index, stratum lithology, linear faults, tectonic complexity, normalized differential water index, residential density, road network density, and land use/land cover. Two hidden layers of BPNN optimized by genetic algorithm (GA) were trained to evaluate the susceptibility grade of geohazards for Xingye. Receiver operating characteristics (ROC) curves, mean square error (MSE), mean of absolute percentage error (MAPE), and field investigation were used to test the validity of the models. Analysis and comparison of the results of different models as indicated by the values of area under curve (AUC) of ROC curves, MSE, and MAPE for five models clearly indicated that the GA‐BPNN model developed in the present study has the highest accuracy (0.9344) and the least errors (0.1925% and 7.3833%) for the susceptibility evaluation and prediction of geohazards in Xingye, followed by the BPNN, support vector machine, random forest, and logistic regression. Moreover, the results also show that the GA has a positive effect on the BPNN model. Furthermore, the GA‐BPNN model developed in the present study can be used for the assessment of geohazards susceptibility in similar regions.