A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Flood susceptibility modelling using advanced ensemble machine learning models

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

With the increasing threat of recurring landslides, susceptibility maps are expected to play a bigger role in promoting our understanding of future landslides and their magnitude. This study describes the development and validation of a spatially explicit deep learning (DL) neural network model for the prediction of landslide susceptibility. A geospatial database was generated based on 217 landslide events from the Muong Lay district (Vietnam), for which a suite of nine landslide conditioning factors was derived. The Relief-F feature selection method was employed to quantify the utility of the conditioning factors for developing the landslide predictive model. Several performance metrics demonstrated that the DL model performed well both in terms of the goodness-of-fit with the training dataset (AUC = 0.90; accuracy = 82%; RMSE = 0.36) and the ability to predict future landslides (AUC = 0.89; accuracy = 82%; RMSE = 0.38). The efficiency of the model was compared to the quadratic discriminant analysis, Fisher's linear discriminant analysis, and multi-layer perceptron neural network. A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over these other models. The insights provided from this study will be valuable for further development of landslide predictive models and spatially explicit assessment of landslide-prone regions around the world.

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.

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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Flash flood is a typical natural hazard that occurs within a short time with high flow velocities and is difficult to predict. In this study, we propose and validate a new soft computing approach that is an integration of an Extreme Learning Machine (ELM) and a Particle Swarm Optimization (PSO), named as PSO-ELM, for the spatial prediction of flash floods. The ELM is used to generate the initial flood model, whereas the PSO was employed to optimize the model. A high frequency tropical typhoon area at Northwest of Vietnam was selected as a case study. In this regard, a geospatial database for the study area was constructed with 654 flash flood locations and 12 influencing factors (elevation, slope, aspect, curvature, toposhade, topographic wetness index, stream power index, stream density, NDVI, soil type, lithology, and rainfall). The model performance was validated using several evaluators such as kappa statistics, root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and area under the ROC curve (AUC-ROC) and compared to three state-of-the-art machine learning techniques, including multilayer perceptron neural networks, support vector machine, and C4.5 decision tree. The results revealed that the PSO-ELM model has high prediction performance (kappa statistics = 0.801, RMSE = 0.281; MAE = 0.079, R2 = 0.829, AUC-ROC = 0.954) and successfully outperformed the three machine learning models. We conclude that the proposed model is a new tool for the prediction of flash flood susceptibility at high frequency tropical typhoon areas.

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area

Nguyen Quang Minh

Papers

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area