Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R²) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R². Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R² compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

     

Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees,Logistic Regression and J48 Decision Trees Methods: A Comparative Study

In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas.     

Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio,certainty factor,and index of entropy

The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning.     

Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier

In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management.     

Computational Intelligence Model for Estimating Intensity of Blast-Induced Ground Vibration in a Mine Based on Imperialist Competitive and Extreme Gradient Boosting Algorithms

Natural Resources Research - In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced ground vibration in a mine based on extreme gradient boosting (XGBoost)...     

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

Natural Resources Research - Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of...     

东南亚断裂构造的演化

本研究项目是采用诸如地质学解释、影像判读和地球物理探测等多种方法相结合进行的。在所得结果的基础上参考了越南国内外同行的一些资料编制了东南亚、越南及邻区的断裂构造图．其比例尺分别为1：4百万和1：1百万。分析所得结果显示出东南亚断裂构造演化的下列情况：1)在现今地质结构方面东南亚是欧亚岩石圈板块的东南部分．由一条消减带围绕．这条消减带的伸展从Myanmer开始,通过Nicobar,Java Timor直到东菲律宾。东南亚被Song Hong(即红河)断裂,Three Pagodas断裂和Hainam-Natuna断裂等2级断裂系统分成3个微板块。2)在早新生代．东南亚是分为5个微板块的。它们的分界断裂中有2个一级断裂(中央东海扩张带和Lupar-Kuching消减带)和3个二级断裂(即上述3个)。3)上述绝大多数二和三级断裂从晚新生代起活化且继承了从早新生代即已发生和发展了的二、三级断裂,但在某些条件下．运动方向却完全变成了相反,尤其是走滑运动的方向。我们的研究结果表明：在这一地区内,盆地、隆起、岩浆侵入、褶皱和局部断裂等构造的形成都取决于这些沿着一、二级走滑断裂的微板块运动。     

Geoheritage values in the Cat Ba islands, Vietnam

Geological wonders have been generally known as natural wonderful products. Resulted from geological processes, geological wonders are diverse in size that have geoheritage values that should be protected from damaging of substance, form and natural development. In a large scale, geological wonders can be geoheritage areas, containing several geodiversity elements that are geologically important or in a smaller scale, they can be geosites of heritage values (or geoheritage sites). In the delimitation of areas, having geoheritage values and the establishment of geoparks, the first thing is to recognise them as geosites and geoheritage areas that indicate great geological values. Besides the Ha Long bay, the world natural heritage with its outstanding aesthetic and geological values, the Cat Ba islands are typical and grandeur karst landscapes formed in tropical condition. Based on the geodiversity elements with their own geoheritage values on aesthetics, uniqueness and grandeur in the Cat Ba islands, the authors have recognised three geoheritage areas: the south cape of the Cat Ba embayment, Tung Gau (shelter), and the Lan Ha bay. Sites where Brachiopods, Crinoids and Tetracorals are exposed on the way through the island are considered as palaeontological geosites. The folds of limestone layers in the northern part of Cat Co 3 beach, with typical turbidite structures in carbonate formations are considered as a lithological geosite. The Devonian-Carboniferous boundary near the Cat Co 3 beach is regarded as a stratigraphical geosite while Que Kem and Turtle islands, etc. are considered as geomorphological geosites.     

Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Developing models to predict on‐site soil erosion and off‐site sediment transport at the agricultural watershed scale represent an on‐going challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub‐surface runoffs in a small hilly watershed (< 1 km²). The semi‐quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates the Manning–Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30 m × 30 m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash–Sutcliffe efficiency (Ef) and correlation coefficient (r²) having values > 0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.