Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest

Natural Resources Research - Blasting is the most popular method for rock fragmentation in open-pit mines. However, the side effects caused by blasting operations include ground vibration, air...     

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.

     

Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network

Natural Resources Research - Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the...     

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)...     

Rapid Shrinking of Glaciers in the Middle Qilian Mountain Region of Northwest China during the Last ～50 Years

During the past five decades, fluctuations of glaciers were reconstructed from historical documents, aerial photographs, and remote sensing data. From 1956 to 2003, 910 glaciers investigated had reduced in area by 21.7% of the 1956 value, with a mean reduction for the individual glacier of 0.10 km2. The relative area reductions of small glaciers were usually higher than those of large ones, which exhibited larger absolute loss, indicating that the small glaciers were more sensitive to climate change than la...     

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.     

Investigating the impacts of typhoon-induced floods on the agriculture in the central region of Vietnam by using hydrological models and satellite data

Flooding associated with landing tropical cyclones (TCs) is one of the major natural hazards in the coastal region of Vietnam. Annually, approximately 5 or 6 TCs make landfall in Vietnam, bringing heavy rains and inducing flooding, particularly to the central coastal region because of its topography and geographic configuration. This study focuses on the modelling of typhoon-induced floods that have resulted in widespread damage to agriculture over the central Thua Thien Hue Province of Vietnam by coupling two well-known hydrological models, KINEROS2 and HEC-RAS (Daniel et al. in Open Hydrol J 5(1), 2011), and using GSMaP (Global Satellite Mapping of Precipitation) data as the satellite rainfall input. Landsat imagery and GIS are also used for mapping and analysing the inundated areas. The discharge and water level from the KINEROS2 and HEC-RAS models displayed acceptable results for the floods modelled from three selected typhoons; both the Nash–Sutcliffe simulation efficiency coefficient (NSE) and the coefficient of determination (R²) were greater than 0.6. The simulated inundation maps of these typhoon-induced floods were compared with those extracted from the Landsat imagery to assess consistency. The result revealed a similar spatial extension of the inundated agricultural areas. This information, together with the forecasted TC movements and associated rainfalls, will be helpful to plan methods for mitigating potential typhoon-induced flooding and damage, particularly damage to agricultural regions.     

Formation process of two massive dams following rainfall-induced deep-seated rapid landslide failures in the Kii Peninsula of Japan

Extreme heavy rainfall due to Typhoon Talas on September 2–4, 2011 in the Kii Peninsula, Japan, triggered numerous floods and landslides. This study investigates the mechanism and the entire process of rainfall-induced deep-seated landslides forming two massive dams in the Kuridaira and Akatani valleys, respectively. The mechanism of the rapid deep-seated landslides is examined through a series of laboratory experiments on samples from sliding surfaces by using undrained high-stress dynamic-loading ring-shear apparatus. The test results indicate that the failure of samples is triggered by excess pore water pressure generation under a shear displacement from 2 to 7 mm with a pore pressure ratio ranging from 0.33 to 0.37. The rapid movement of landslides is mainly attributed to high mobility due to the liquefaction behavior of both sandstone-rich and shale samples. Geomorphic settings and landslide mobility are major contributing factors to the dam formation. Additionally, shear displacement control tests show that a certain amount of shear displacement between 2 and 7 mm along the sliding surfaces of the gravitationally deformed slopes might have led to the failures. Importantly, computer simulation with LS-RAPID software using input parameters obtained from physical experiments is employed to interpret the entire formation process of the abovementioned two landslide dams. The simulation results are examined in accordance with the observed on-site geomorphic features and recorded data to explain the possibility of sliding processes. The results further point out that local failures are initiated from the lower middle part of the landslide bodies where the geological boundary exists. This condition most probably influences the landslide initiation in the two case studies. This research is therefore helpful for hazard assessment of slopes that are susceptible to deep-seated landslides and other sequential processes in areas with geology and geomorphology similar to that of the Kii Peninsula.     

Analysis of snowmelt model for flood forecast for water in arid zone: case of Tarim River in Northwest China

Field studies were carried out in Tarim River Basin, Northwest China for analysis of snowmelt model for flood forecast for a river in arid zone. Snow is a major source for water availability in arid zone of Northwest China where 50% of snow cover withdrew by sublimation during dry and cold climatic condition. The analysis of weekly forecast of daily discharges was helped by the temperature index model, ARIMA model for temperature and flow, D-IUH runoff model and D-IUH model estimation where the temperature forecast was used as driving variable; the numerical simulations were carried out using SUSA^® software for testing the sensitivity of the D-IUH to the input values of the parameter and an analysis of the forecast results against the set of input parameters resulted in a determination coefficient R ² = 0.5. The standard deviation was 3.28 and the mean for the Tarim River was 5.37 (mm d^?1) implying that the forecasted data is in strong agreement with the observed data. The combination of methods is better useful for calculation in order to avoid errors of appreciation.