Estimation of Soil Depth Using Bayesian Maximum Entropy Method.

Soil depth plays an important role in landslide disaster prevention and is a key factor in slopeland development and management. Existing soil depth maps are outdated and incomplete in Taiwan. There is a need to improve the accuracy of the map. The Kriging method, one of the most frequently adopted estimation approaches for soil depth, has room for accuracy improvements. An appropriate soil depth estimation method is proposed, in which soil depth is estimated using Bayesian Maximum Entropy method (BME) considering space distribution of measured soil depth and impact of physiographic factors. BME divides analysis data into groups of deterministic and probabilistic data. The deterministic part are soil depth measurements in a given area and the probabilistic part contains soil depth estimated by a machine learning-based soil depth estimation model based on physiographic factors including slope, aspect, profile curvature, plan curvature, and topographic wetness index. Accuracy of estimates calculated by soil depth grading, very shallow (<20 cm), shallow (20-50 cm), deep (50-90 cm), and very deep (>90 cm), suggests that BME is superior to the Kriging method with estimation accuracy up to 82.94%. The soil depth distribution map of Hsinchu, Taiwan made by BME with a soil depth error of ±5.62 cm provides a promising outcome which is useful in future applications, especially for locations without soil depth data.

Fluvial flood adaptation using nature-based solutions: A comprehensive and effective assessment of hydro-meteorological risks.

The growing prominence of Nature-based Solutions (NbS) for disaster risk reduction (DRR) has sparked increased interest. This study is motivated by the need to establish a quantifiable and standardized method for assessing the risks mitigated by NbS in engineering applications. The goal is to establish a comprehensive and effective system framework for assessing hydro-meteorological risks related to NbS in engineering applications. The proposed framework considers flood disaster mechanisms, uncertain factors, and ecosystem services, integrating them to comprehensively assess the benefits of NbS. Specifically, 2-D hydraulic analysis and an in-house adaptive Kriging-based reliability analysis are developed and applied to establish flood prevention standards for NbS. Additionally, the InVEST toolkit is utilized to evaluate ecosystem services. To demonstrate the applicability of the framework, the Baoli River Watershed located in Pingtung County of Taiwan is selected as a case study. It is found that NbS can effectively withstand a 25-year return period flood and reduce flooding on agricultural land by 46.03 %. Furthermore, the probability of flooding decreased from 100 % to 27 % for a 20-year return period flood. NbS was found to provide approximately NT$1.20-4.65 million more in total benefit value compared to the engineering governance strategy. The supporting source codes are available at https://github.com/johnthedy/Adaptive-Kriging-Using-PSO-HHs-in-HECRAS3D.git.

A probabilistic bridge safety evaluation against floods.

To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation.