Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh.

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

Hydrodynamic and topography based cellular automaton model for simulating debris flow run-out extent and entrainment behavior.

As for late, studies have indicated that cellular automaton (CA) models are among the most effective solutions for simulating the extent of debris-flow run-out. However, it is currently difficult to effectively simulate both the inundated area and the erosion pattern of the debris flow process. This difficulty is caused by the lack of detailing regarding debris flow hydrodynamics as the primary concern of most CA-based models is the topographic gradient of the gully. In this study, we propose a two-dimensional Monte Carlo simulation-based CA model with hydrodynamic methods describing debris-flow behavior to address these problems. Herein, a topography function concerning slope gradient and bed roughness, and a persistence function regarding flow inertia, are combined to improve the flow routing algorithm for better determining the run-out extent of debris flow. Hydraulic links and discharge exchange between neighboring cells using sink-filling approach, as well as the bed sediment entrainment function, are incorporated into the CA model to describe the mass migration process along the flow path. To verify the performance of our proposed model, we further select the 2010 Yohutagawa debris flow event in Japan as a case study. The results indicate that the proposed model better simulates the complex dynamic process of debris flow.

Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine.

A gradient boosting machine (GBM) was developed to model the susceptibility of debris flow in Sichuan, Southwest China for risk management. A total of 3839 events of debris flow during 1949-2017 were compiled from the Sichuan Geo-Environment Monitoring program, field surveys, and satellite imagery interpretation. In the cross-validation, the GBM showed better performance, with the prediction accuracy of 82.0% and area under curve of 0.88, than the benchmark models, including the Logistic Regression, the K-Nearest Neighbor, the Support Vector Machine, and the Artificial Neural Network. The elevation range, precipitation, and aridity index played the most important role in determining the susceptibility. In addition, the water erosion intensity, road construction, channel gradient, and human settlement sites also largely contributed to the formation of debris flow. The susceptibility map produced by the GBM shows that the spatial distributions of high-susceptibility watersheds were highly coupled with the locations of the topographical extreme belt, fault zone, seismic belt, and dry valleys. This study provides critical information for risk mitigating and prevention of debris flow.