Logistic regression model for predicting the failure probability of a landslide dam

Landslides may obstruct river flow and result in landslide dams; they occur in many regions of the world. The formation and disappearance of natural lakes involve a complex earth–surface process. According to the lessons learned from many historical cases, landslide dams usually break down rapidly soon after the formation of the lake. Regarding hazard mitigation, prompt evaluation of the stability of the landslide dam is crucial. Based on a Japanese dataset, this study utilized the logistic regression method and the jack-knife technique to identify the important geomorphic variables, including peak flow (or catchment area), dam height, width and length in sequence, affecting the stability of landslide dams. The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models. Accordingly, the failure probability of a landslide dam can also be evaluated based on this approach. Ten landslide dams (formed after the 1999 Chi-Chi Earthquake, the 2008 Wenchuan Earthquake and 2009 Typhoon Morakot) with complete dam geometry records were adopted as examples of evaluating the failure probability. The stable Tsao-Ling landslide dam, which was induced by the Chi-Chi earthquake, has a failure probability of 27.68% using a model incorporating the catchment area and dam geometry. On the contrary, the Tangjiashan landslide dam, which was artificially breached soon after its formation during the Wenchuan earthquake, has a failure probability as high as 99.54%. Typhoon Morakot induced the Siaolin landslide dam, which was breached within one hour after its formation and has a failure probability of 71.09%. Notably, the failure probability of the earthquake induced cases is reduced if the catchment area in the prediction model is replaced by the peak flow of the dammed stream for these cases. In contrast, the predicted failure probability of the heavy rainfall-induced case increases if the high flow rate of the dammed stream is incorporated into the prediction model. Consequently, it is suggested that the prediction model using the peak flow as causative factor should be used to evaluate the stability of a landslide dam if the peak flow is available. Together with an estimation of the impact of an outburst flood from a landslide-dammed lake, the failure probability of the landslide dam predicted by the proposed logistic regression model could be useful for evaluating the related risk.     

Regional daily baseflow prediction

The purposes of this study are to identify the bias of applying the analysis of a log–log plot of baseflow and to derive an equation to describe successive regional mean baseflow. The function ?dQ/dt = a Q^b has been used to describe baseflow in many studies that obtain the values of a and b from the log–log plot. According to analysis in this study, the value of 1 can be assigned to b in two boundary conditions, but the parameter a is proved to be related to the depth of water table and starting time of recession and thus different values of a may be found for different recession events. This paper points out that no single regression line can be obtained by plotting all baseflow data on a log–log diagram. Instead, there should be parallel lines, and each for a recession event. It implies that no single set of parameters a and b can be applied to predict baseflow. Thus, a new equation describing the relationship between three successive mean baseflows was derived in this study. The bias in the analysis of the log–log plot and the ability of the derived equation to predict baseflow were verified for five watersheds in Taiwan. Results indicate that the formula of mean baseflow prediction can provide reasonable estimates of flows with a leading time of 6 days. Furthermore, stream flows of the Tonkawa creek watershed in USA were used to verify that using average flows can result in better predictions than using instantaneous flows. Copyright © 2004 John Wiley & Sons, Ltd.     

Modified one-phase-low-pH method for bacteria or enzyme-induced carbonate precipitation for soil improvement

Acta Geotechnica - One of the latest developments in biocementation is the use of one-phase-low-pH MICP or EICP method as a more effective and efficient alternative to the traditional two-phase...     

A Detailed Study on the Equal Arrival Time Surface Effect in Gamma-Ray Burst Afterglows

Due to the relativistic motion of gamma-ray burst remnant and its deceleration in the circumburst medium, the equal arrival time surfaces at any moment are not spherical, rather, they are distorted ellipsoids. This will leave some imprints in the afterglows. We study the effect of equal arrival time surfaces numerically for various circumstances, i.e., isotropic fireballs, collimated jets, density jumps and energy injection events. For each case, a direct comparison is made between including and not including the effect. For isotropic fireballs and jets viewed on axis, the effect slightly hardens the spectra and postpones the peak time of the afterglows, but does not change the shapes of the spectra and light curves significantly. In the cases of a density jump or an energy injection, the effect smears out the variations in the afterglows markedly.     

Uncertainties in real‐time flood forecasting with neural networks

Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.     

An intelligent expert system shell for knowledge-based Geographical Information Systems: 1. The tools

Abstract

An intelligent expert system shell for the development of knowledge-based geographical information systems (GIS) is examined in this two-part article. Basic concepts and the overall architecture of the shell are discussed in the present part. Fuzzy logic and expert systems technology are demonstrated to be appropriate methods for approximating human reasoning and enhancing the level of intelligence in GIS. The shell can be employed as an effective and efficient tool for developing knowledge-based GIS.     

A rough set approach to the discovery of classification rules in spatial data

This paper proposes a novel rough set approach to discover classification rules in real‐valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real‐valued or integer‐valued decision system into an interval‐valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real‐life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.     

Glacially derived material in an Inner Mongolian desert lake during Marine Isotope Stage 2

Establishing the precise timing of continental glacial dynamics and abrupt high‐latitude climate events is crucial to understanding the causes of global climate change. Here we present multi‐proxy records in a lake sediment core from arid Inner Mongolia (Wuliangsuhai Lake) that show two distinct glacially derived sedimentation events at ～26.2–21.8 and ～17.3–11.5k cal a BP. Fine sediments from the Last Glacial Maximum separate these glacially derived coarse sediments. Within these intervals, the occurrence of granite clasts at ～24–23.5, 17.3–17 and 15.6–14.1k cal a BP implies either sediment discharge by meltwater as well as strong current flow in the Yellow River and/or sediment influx through hill‐slope mass wasting and landsliding from the nearby Yin Mountains. Surface microfeatures of quartz grains and spot elemental analysis of black specks in these intervals, however, indicate that physical weathering is dominant and that the provenance of the rocks is probably from a glacial source. To the best of our knowledge, this is the first time glacier‐derived materials have been detected in any desert lake in the Yellow River basin. The occurrence of granite clasts roughly correlates with Heinrich events in the North Atlantic, suggesting synchronous ice sheet dynamics in high‐ and mid‐latitude regions during the Last Glacial period. Although our data provide unprecedented evidence for the influence of glacier‐related processes in arid Inner Mongolia, further well‐dated records are clearly needed to re‐evaluate the correlative inference drawn between granite clast layers in Wuliangsuhai Lake and Heinrich events in the North Atlantic. Copyright © 2012 John Wiley & Sons, Ltd.     

On the nonlinear waves in a developing process

Based on the discrete form of the main governing equation derived, a single wave as the main motion of the instability analysis was found. This solution gives the whole process from the initial stage to the nonlinear equilibrium state. Next we examined the instability of the main motion above-mentioned in the initial stage and showed the instability properties of a developing process. Contribution No. 987 from the Institute of Oceanology, Academia Sinica.     

不确定分析技术中的多项式正态转换方法研究

不确定性及可靠性分析技术已广泛应用于水利及土木系统工程分析中，可靠性计算的核心之一是对随机变量进行正态转换。对土木工程系统不确定性及可靠性分析中有着潜在应用前景的非正态随机变量的多项式正态转换方法进行了研究，通过在各种条件下非正态随机变量的正态多项式转换成果之评价，发现在水利及土木工程可靠性分析中常用的许多分布可以保留其自身的比较好的特征。