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Abstract. Rainfall is one of the most important factors controlling landslide deformation and failure. State-of-art rainfall data collection is a common practice in modern landslide research world-wide. Nevertheless, in spite of the availability of high-accuracy rainfall data, it is not a trivial process to diligently incorporate rainfall data in predicting landslide stability due to large quantity, tremendous variety, and wealth multiplicity of rainfall data. Up to date, most of the pre-process procedure of rainfall data only use mean value, maxima and minima to characterize the rainfall feature. This practice significantly overlooks many important and intrinsic features contained in the rainfall data. In this paper, we employ cluster analysis (CA)-based feature analysis to rainfall data for rainfall feature extraction. This method effectively extracts the most significant features of a rainfall sequence and greatly reduced rainfall data quantities. Meanwhile it also improves rainfall data availability. For showing the efficiency of using the CA characterized rainfall data input, we present three schemes to input rainfall data in back propagation (BP) neural network to forecast landslide displacement. These three schemes are: the original daily rainfall, monthly rainfall, and CA extracted rainfall features. Based on the examination of the root mean square error (RMSE) of the landslide displacement prediction, it is clear that using the CA extracted rainfall features input significantly improve the ability of accurate landslide prediction.

Geo-anomalies with complex structures in the earth's crust may be defined as preferable hydrothermal ore-forming targets. The separation and explanation of anomaly from geological background have a profound influence on the analysis of geological evolution and the ore-forming process. Usually one of the key steps to identify favorable exploration targets is to determine the threshold to separate anomaly from geological background. In this paper, the singularity theory and concentration–area (C–A) fractal method was applied in determination of the threshold of geo-anomalies. According to the thresholds, four singular maps can be each divided into two segments. Each of them is correlated to the anomaly and background of the geological objects (e.g., faults, fault intersections and contacts), which reveals that the various geo-anomalies can be characterized by the singularities. The results indicate that the local singularity method can be used to identify the weak anomalies in a low background. Logistic regression model was used to combine geo-singularity maps for mineral potential mapping, which provides a useful input for further mineral exploration in the Nanling tungsten polymetallic belt, South China