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

Abstract

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.

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

“…The TDepE model was trained by learning data consisting of the age, the length, and the width of the fish (pixel), as well as the actual depth of the fish. In terms of the performance, the obtained TDepE models based on LR, RFR with 2 level maximum depth, and SVR with radial basis function (RBF) methods [ 38 , 39 ] are illustrated in Table 2 and Figure 10 , respectively. The RBF kernel [ 40 ] is expressed in Equation (17) as: where denotes the variance as the hyperparameter and represents the Euclidean ( L₂ -norm) Distance between two points X 1 and X 2 .…”

Algoritmos de Aprendizado de Máquina Aplicados na Avaliação do Volume de Chuvas na Cidade do Recife

“…Percebe-se ainda no artigo que os autores fazem uma análise sobre a perspectiva de dados de longo prazo, deixando uma lacuna para cenários de curto prazo. Como proposta futura, eles se propõem a considerar modelos de aprendizagem profunda (Deep Learning) [12], no contexto de classificac ¸ão de séries temporais [13] e interac ¸ão homem-máquina [14], visando uma estrutura que possibilite aos usuários encontrar uma soluc ¸ão iterativa que auxilie no processo de previsão de inundac ¸ão.…”

Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

“…Comparisons of several machine learning algorithms for landslide mapping were compared in [22]- [24]. The area under receiver operating characteristics curve (AUC) was the main metrics to assess the model performance in the literature [25]- [27]. The objectives of this paper are; assessing ML and AI performance in classifying whether some rainfall properties would trigger a landslide event or not; testing a data transformation prior to ML and AI model building and whether it can increase the performance of models.…”