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...     

Assessing Possible Impacts of Climate Change on Species Important for Forestry in Vietnam

The likely effects on two tree species of a range of scenarios of climatic and atmospheric change expected by the year 2050 are investigated using a climatic mapping program, a simple simulation model and a process-based simulation model. Styrax tonkinensis is a native species for which relatively little information is available. Acacia mangium is an introduced species, which is important for pulp production in several other countries, and for which there is considerable information for growth and utilization. A climatic mapping program is used to show areas which may be suitable for these species under present and predicted conditions. Two simulation models are used to investigate likely effects on productivity of the two species for a range of climatic change scenarios for Hanoi and Ho Chi Minh City. The estimated changes in production are predicted to be relatively small, though uncertainities associated with the simulations are quite high. However, the models highlight areas where more data are needed and also suggest some key regions in Vietnam which would be worth monitoring to detect early signs of the effects of climatic and atmospheric change.     

Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques

Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R²), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R²?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R², and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.

     

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

Natural Resources Research - Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of...     

Prediction of Blast-Induced Ground Vibration in Open-Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules

Natural Resources Research - In this paper, blast-induced ground vibration (BIGV) was considered as the primary objective, and a new artificial intelligence system was proposed to predict BIGV with...     

Mean and variance evolutions of the hot and cold temperatures in Europe

In this paper, we examine the trends of temperature series in Europe, for the mean as well as for the variance in hot and cold seasons. To do so, we use as long and homogenous series as possible, provided by the European Climate Assessment and Dataset project for different locations in Europe, as well as the European ENSEMBLES project gridded dataset and the ERA40 reanalysis. We provide a definition of trends that we keep as intrinsic as possible and apply non-parametric statistical methods to analyse them. Obtained results show a clear link between trends in mean and variance of the whole series of hot or cold temperatures: in general, variance increases when the absolute value of temperature increases, i.e. with increasing summer temperature and decreasing winter temperature. This link is reinforced in locations where winter and summer climate has more variability. In very cold or very warm climates, the variability is lower and the link between the trends is weaker. We performed the same analysis on outputs of six climate models proposed by European teams for the 1961–2000 period (1950–2000 for one model), available through the PCMDI portal for the IPCC fourth assessment climate model simulations. The models generally perform poorly and have difficulties in capturing the relation between the two trends, especially in summer.     

Toward state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis,radial basis function neural network,and brainstorm optimization

Acta Geotechnica - This study aims to propose state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis, artificial neural...     

Non-stationary future return levels for extreme rainfall over Extremadura (southwestern Iberian Peninsula)

Based on a previous study for temperature, a new method for the calculation of non-stationary return levels for extreme rainfall is described and applied to Extremadura, a region of southwestern Spain, using the peaks-over-threshold approach. Both all-days and rainy-days-only datasets were considered and the 20-year return levels expected in 2020 were estimated taking different trends into account: first, for all days, considering a time-dependent threshold and the trend in the scale parameter of the generalized Pareto distribution; and second, for rainy days only, considering how the mean, variance, and number of rainy days evolve. Generally, the changes in mean, variance and number of rainy days can explain the observed trends in extremes, and their extrapolation gives more robust estimations. The results point to a decrease of future return levels in 2020 for spring and winter, but an increase for autumn.     

Improving watershed management practices in humid regions

In many parts of the world, watershed management practices have been extremely effective. However, implementation of soil and water conservation technologies in the humid African highlands, while beneficial in the short term, were remarkably unsuccessful in the long term. Insights from community knowledge perspectives have revealed that alternative methods are needed. Although conservation practices are designed to conserve water in semi‐arid areas, safely draining excess water is needed in humid areas. The objective of this paper is to review current watershed management approaches used in humid regions as exemplified by those used in Ethiopian highlands and then based on these findings propose more effective practices. Although current government sponsored practices primarily protect the hillsides, direct run‐off is generated from areas that become saturated on valley bottoms near rivers and on specific parts of the hillsides with degraded soils (or with highly permeable surface soils) and with perched water tables on slowly permeable horizons at shallow depths. In these areas, direct run‐off is increasing with deforestation and the soil degradation, demanding additional drainage ways that evolve in the form of gullies. Therefore, watershed management interventions for erosion control should prioritize revegetation of degraded areas, increasing sustainable infiltration, and rehabilitating gullies situated at saturated bottomlands.     

Multipurpose agroforestry as a climate change resiliency option for farmers: an example of local adaptation in Vietnam

Increasing frequency, intensity and duration of severe weather events are posing major challenges to global food security and livelihoods of rural people. Agriculture has evolved through adaptation to local circumstances for thousands of years. Local experience in responding to severe weather conditions, accumulated over generations and centuries, is valuable for developing adaptation options to current climate change. This study aimed to: (i) identify tree species that reduce vulnerability of cropping systems under climate variability; and (ii) develop a method for rapidly assessing vulnerability and exploring strategies of smallholder farmers in rural areas exposed to climate variability. Participatory Rural Appraisal methods in combination with Geographical Information Systems tools and statistical analysis of meteorological data were used to evaluate local vulnerability to climate change and to investigate local adaptation measures in two selected villages in Vietnam, one of the countries most vulnerable to climate change. The low predictability of severe weather events makes food crops, especially grain production, insecure. This study shows that while rice and rain-fed crops suffered over 40 % yield losses in years of extreme drought or flood, tree-based systems and cattle were less affected. 13 tree species performed well under the harsh local climate conditions in home and forest gardens to provide income, food, feed and other environmental benefits. Thus, this research suggests that maintenance and enhancement of locally evolved agroforestry systems, with high resilience and multiple benefits, can contribute to climate change adaptation.