Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models.

Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations-Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

Stabilized High Clay Content Lateritic Soil Using Cement-FGD Gypsum Mixtures for Road Subbase Applications.

With a lack of standard lateritic soil for use in road construction, suitable economical and sustainable soil-stabilization techniques are in demand. This study aimed to examine flue gas desulfurization (FGD) gypsum, a by-product of coal power plants, for use in soil-cement stabilization, specifically for ability to strengthen poor high-clay, lateritic soil but with a lower cement content. A series of compaction tests and unconfined compressive strength (UCS) tests were performed in conjunction with scanning electron microscope (SEM) analyses. Therefore, the strength development and the role of FGD gypsum in the soil-cement-FGD gypsum mixtures with varying cement and FGD gypsum contents were characterized in this study. The study results showed that adding FGD gypsum can enhance the strength of the stabilized substandard lateritic soil. Extra FGD gypsum added to the cement hydration system provided more sulfate ions, leading to the formation of ettringite and monosulfate, which are the hardening cementitious products from the cement hydration reaction. Both products contributed to the strength gain of the soil-cement-FGD gypsum material. However, the strength can be reduced when too much FGD gypsum is added because the undissolved gypsum has a weak structure. Examinations of FGD gypsum in the soil-cement-FGD gypsum mixtures by SEM confirmed that adding FGD gypsum can reduce the cement content in a soil-cement mix to achieve a given UCS value.