Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.
Building Safety Research Center & Seismic Safety Research Center, Korea Institute of Civil Engineering and Building Technology, Daejeon 34141, Korea.
Sensors (Basel). 2020 Mar 13;20(6):1611. doi: 10.3390/s20061611.
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.
土壤含水量是滑坡灾害最重要的物理指标之一。因此，快速、非破坏性地对土壤进行分类，并确定或预测含水量，是滑坡灾害探测的重要任务。我们研究了从韩国首尔采集的 162 个花岗岩土壤样本在可见和近红外区域（400-1000nm）的高光谱信息。首先，使用连续投影算法从预处理光谱数据中提取有效波长，以开发分类模型。使用灰度共生矩阵提取纹理变量，并使用支持向量机建立校准模型和预测模型。结果表明，通过结合有效波长和纹理特征数据集进行建模，可达到 89.8%的最优正确分类率。利用所开发的分类模型，构建了一个用于预测土壤含水量的人工神经网络（ANN）模型。输入参数由孟塞尔土壤颜色、反射率（近红外）面积和干单位重量组成。所开发的 ANN 模型的含水量预测精度通过决定系数和平均绝对百分比误差分别为 0.91 和 10.1%得到验证。
