Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Natural Hazards - Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high...     

Morphological Change in the Northern Red River Delta,Vietnam

Coastal erosion has become a worldwide concern, typically in the densely populated Asian mega-river deltas. Severe coastal erosion in the southern Red River Delta(RRD) has been intensively studied. Coastal morphological change in the northern RRD was examined in detail through DEM(Digital Elevation Model) analysis based on time series of bathymetrical maps(1965–2004) and Landsat images(1975–2015) in this study. The results show that the northern RRD is featured by rapid coastal accretion in the past few decades, although suspended sediment flux has dropped by roughly 60% after the completeness of Hoa Binh Dam(HBD) in 1988 and relative sea level rose at 1.9 mm yr~(-1). However, accretion at the outer part of subtidal shoals and platforms was observed to slow down quickly or even turned into erosion in the last two decades. The resuspended sediments from the erosion zone can be transported landward to replenish the inner coastal zone, keeping the latter accretion in the near future to compensate for the sediment discharge decrease from the river. However, this lag effect should be terminated soon if other adverse effects go worse, e.g., damming rivers, sea-level rising, strengthening storms, land reclamation and other poor-designed coastal engineering. Coastal planners and managers should pay full attention to these changes.     

Evaluation and comparison of LogitBoost Ensemble,Fisher’s Linear Discriminant Analysis,logistic regression and support vector machines methods for landslide susceptibility mapping

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.     

Establishing an early warning system for flash floods in Hoang Su Phi District,Ha Giang Province,Vietnam

Hoang Su Phi is a mountainous district in Ha Giang province, Vietnam. When the rainy season arrives in these parts, flash floods (besides landslides), frequently occur in many areas, seriously affecting the socio‐economic condition of the district. Therefore, in this paper, we have developed an early warning system for flash floods, established based on a geomorphological and hydrological approach. The basic principle underlying this system is the fact that flash floods will often occur where there is high potential risk with sufficient rainfall. In the model, eight parameters of the basin were used to build a potential flash flood map. Using the spatial processing module in an open source software, early predictions from automatic weather stations were interpolated and processed online to produce a potential risk map. Depending on the threshold of precipitation values, the results determined locations where flash flood may occur at various flash flood risk indices (FFRI). The system may be applied to support provision of early flash flood warning up to 1?6 days in advance in the district, allowing the local government ample time to make appropriate decisions on the prevention and/or mitigation of damages caused by flash flood hazards.     

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

Natural Resources Research - Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue...     

A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network

Natural Resources Research - The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench...     

Hyperstatic reaction method for calculations of tunnels with horseshoe-shaped cross-section under the impact of earthquakes

Tunnels are now an integral part of the infrastructure in major cities around the world. For many reasons, these tunnels have horseshoe-shaped cross-sections with round top and flat bottom. This paper presents some improvements to the use of the Hyperstatic Reaction Method-HRM for analysing tunnels with horseshoe-shaped cross-sections when these tunnels operate under the influence of earthquakes, particularly in cases when the tunnel lining is a continuous lining. The analysis used parameters of a tunnel from the Hanoi metro system, as well as parameters of the strongest earthquake that may occur in the central Hanoi area in the improved HRM and 2 D numerical methods using the ABAQUS software. On the basis of the results obtained, the paper gives conclusions about the HRM methodology when it is used to calculate tunnels that have horseshoe cross-sections operating under the influence of earthquakes.     

Study the elastic properties and the anisotropy of rocks using different machine learning methods

This paper aims to demonstrate that the elastic stiffnesses and the anisotropic parameters of rocks can be accurately predicted from geophysical features such as the porosity, the density, the compression stress, the pore pressure and the burial depth using relevant machine learning methods. It also suggests that the extreme gradient boosting method is the best method for this purpose. It is more accurate, extremely faster to train and more robust than the artificial neural networks and the support vector machine methods. Very high R-squared scores was obtained for the predicted elastic stiffnesses of a relevant dataset that is available in the literature. This dataset contains different types of rocks, and the values of the features are in large ranges. An optimal set of parameters was obtained by considering an appropriate sensitivity analysis. The optimized model is very easy to implement in Python for practical applications.     

Analysis and control of vibration of ropes in a high-rise elevator under earthquake excitation

In this research, the vibration of elevator ropes, including the main rope and compensation rope are investigated simultaneously in a high-rise elevator system under earthquake excitation. Moreover, the paper presents a new control method to restrain the sway of both ropes. This study considers varying rope lengths during elevator operation which cause other system parameters such as natural frequency, and damping ratio to be time-variant variables. The dynamics of the ropes are analyzed by solving the governing non-stationary, nonlinear equation numerically. In order to mitigate the vibration of ropes in several motion conditions, particularly upwards movement, downward movement, stopped at the lowest position, and stopped at the highest position, an active equipment is installed at the compensation sheave. The stability of the system using the controller is analyzed at four states: without disturbance and static car, without disturbance and mobile car, including disturbance and static car, and including disturbance and mobile car. The efficiency of the controller used for dampening the vibration of elevator ropes is validated by numerical simulation results.     

情理整合视角下旅游者环境责任行为与 意愿悖离研究——基于Logistic-ISM模型

基于对岳麓山景区447位旅游者的实地调研,运用拓展的计划行为理论并结合Logistic回归模型,分析影响两者发生悖离的因素,并进一步利用ISM解释结构模型分析各因素之间的逻辑关系.从Logistic回归结果看,旅游者的行为态度、感知行为控制等相关理性认知越好,发生悖离的可能性越低;旅游者积极环境情感和消极环境情感对悖离...