Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
<p>A centrifuge model test platform was designed and developed to verify the critical continuous rainfalls triggering shallow landslides in natural slopes. Based on literature reviews, in-situ dimensions of shallow landslides on natural slopes were determined to 40 m (Length) &#215; 16 m (Width) &#215; 2 m (Depth) on average. In consequence, considering the model mounting space of the centrifuge test facility, a gravity level was decided (N = 40g) so that the length of a model slope equals 1 m according to scaling law. The width and depth of the model slope were hence determined to 0.4 m and 0.05 m, respectively. On the other hand, a rainfall simulator comprised of a series of air-atomizing spray nozzles was designed and developed considering scaling laws of rainfall infiltration and subsurface water flows. As a simulation result in a 40g condition, rainfall dispersions reduced and its trajectory bending induced by Coriolis&#8217; force was almost vanished. After the development of centrifuge model test platform, several 1g performance tests of the rainfall simulator were conducted to test the spatial uniformity of rainfall distributions and fit the conditions of applying water and air pressures to rainfall intensities. The study also presents preliminary test results of shallow landslides in a 1g condition conducted to find and solve errors and unexpected problems before mounting the platform to the centrifuge test facility.</p>
<p>Soil water content is one of the most common physical parameters that cause landslides or debris flow. Therefore, it is of very importance to determine or predict the water content variation due to infiltration of rainfall quickly and non-destructively. This study investigates the hyperspectral informations in the visible near-infrared regions (VNIR, 400nm~1000nm) of different samples of granite soils possessing varying water contents. Totally 162 granite samples were taken from 3 mountain areas. A Partial Least Squares Regression (PLSR) analysis was applied to develop calibration models and prediction models.&#160; In the water content variation prediction model, the Area of &#8203;&#8203;Reflectance(Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The results demonstrate that the hyperspectral camera combined with the PLSR model can be a useful and non-destructive tool for the determination of soil water content variation in the weathered granite soils that could be applied to the evaluation of possible instable area in a mountain site.</p>
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