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This paper analyzes recreation demand on the fringe of an urban center in Norway. Specifically, it investigates the potential impact of wind energy projects on local recreation values. Recreation areas near cities are often scarce and under increasing pressures from growing populations with associated real-estate development and expanding or emerging industries. Hence, public planners and policy makers who seek to make optimal resource management decisions need information on all opportunity costs, including those associated with diminished recreational access or quality. Two separate case studies utilize the travel cost method (TCM) to derive baseline recreation demands. Supplementary information from contingent behavior (CB) scenarios provides identification of the impact of wind turbines on recreation benefits. For a unique comparison, one case examines a possible inland wind farm near a popular local mountain area, whereas the other case examines an offshore wind farm near local beaches. Econometric estimations are performed in a joint revealed preference (RP) – stated preference (SP) pseudo-panel count-data framework. The analysis generates baseline consumer surplus estimates in the range of NOK 70–155 per trip and indicates that the wind turbines would have negative impacts that are both statistically and economically significant.

Coefficient of consolidation (Cv) is a measure of compressibility of soil. This coefficient is an important parameter which is used in the design of foundation of civil engineering structures and also in the slope stability analysis. In this study, the main objective is to propose a hybrid machine learning approach named MLP-BBO for the prediction of the coefficient of consolidation of soft soil. This method is based on Multi-layer Perceptron Neural Network (MLP) and Biogeography-based Optimization (BBO). For this, a total number of 164 soil samples were collected from the Tan Vu–Lach Huyen highway project and Ha Noi–Hai Phong highway project sites. In the modeling, input parameters used include clay (%), moisture content (%), liquid limit (%), plastic limit (%), plasticity index (%), and liquidity index (%). To validate performance of the models, standard methods such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (r) were used. For the comparison of performance of the models, popular machine learning methods were used namely Backpropagation Multi-layer Perceptron Neural Networks (Bp-MLP Neural Nets), Radial Basis Functions Neural Networks (RBF-Neural Nets), Gaussian Process (GP), M5 Tree, and Support Vector Regression (SVR). Results of the model study indicate that the proposed method MLP-BBO has the highest predictive capability (RMSE = 0.397, MAE = 0.302 and r = 0.827), followed by the SVR (RMSE = 0.403, MAE = 0.299 and r = 0.819), the Bp-MLP Neural Nets (RMSE = 0.478, MAE = 0.398 and r = 0.805), the RBF-Neural Nets (RMSE = 0.412, MAE = 0.281 and r = 0.804), the GP (RMSE = 0.440, MAE = 0.301 and r = 0.777), and the M5 Tree (RMSE = 0.494, MAE = 0.322 and r = 0.728), in the prediction of Cv, respectively. Therefore, it can be concluded that though performance of all applied models is good in the prediction of the coefficient of consolidation of soil but performance of the MLP-BBO is the best.

Groundwater plays a key role in public water supplies around the world, and consideration of the social dimension is a key issue in sustainable groundwater management. In Hanoi, the capital of Vietnam, the situation of groundwater resources is disastrous because the quality has seriously degraded recently. Hence, in this study, a social sustainability assessment framework of groundwater resources with a focus on Hanoi is proposed for the first time. An analytical hierarchy process (AHP) approach was used to generate the main components (aspects and indicators) of this framework, which has been considered one of the most challenging tasks in AHP sustainability applications. To overcome these challenging tasks, we carefully reviewed and explored the current problems of Hanoi groundwater resources to propose 3 main aspects (quantity, quality, and management) and appropriately selected their 13 sustainability indicators for this target area. We introduce a sustainability index function (SIF) for indicators to clarify the relationship between each indicator value and its sustainability index. Furthermore, we consider both the conventional linear relationship, as it is usually developed in the literature, as well as a non-linear SIF to arrive at a reasonable sustainability assessment. For the Hanoi case study, the sustainability indices obtained using a combined linear and non-linear SIF method reflect the current problems well, that is, the social sustainability assessment is closer to reality. The sustainability indices of the quantity, quality, and management aspects of the groundwater were appropriately assessed as good, poor, and acceptable, respectively, resulting in Hanoi being rated at the acceptable level in the final social sustainability assessment. This result reveals that the population of Hanoi is satisfied with the quantity of water but dissatisfied with its current poor quality and its relatively high price.

The mining sector faces some of the most difficult sustainability challenges of any industrial sector. The mining sector is remarkably significant to the development of the Asia-Pacific Economic Cooperation (APEC) region, because the majority of global trade and investment in this region depends on this sector. Therefore, the assessment of mining sector sustainability at national and global scales in APEC economies is an important task. So far, however, mining sector sustainability assessment frameworks have only been developed at small scales, such as companies and cities. In this study, therefore, it is proposed an indicator-based sustainability assessment framework (iSAF) to assess mining sector sustainability at national and global scales. In this framework, fuzzy logic was utilized to adequately deal with the uncertainty and vagueness of human expressions. The conventional fuzzy scales were suitably modified in the proposed framework to facilitate confident decision-making by the relevant stakeholders. In order to avoid repeatedly conducting the judgment until the acceptable consistency was obtained, iSAF implemented an innovative theory to deal with unacceptable consistencies in judgment. The proposed iSAF was applied to the mining sector of APEC economies using data from the APEC 2010 Project. As for the results, three important criteria including economic, environmental, and social criteria and twenty significant indicators were appropriately selected to cover the actual situation in the sector. The results showed that iSAF was a suitable framework for preventing uncertainty and vagueness in decision-making. iSAF was found to provide crucial support to decision-makers, not only in identifying and structuring the main components contributing to sustainability, but also in pointing out the factors in which significant investment should be made to effectively improve sustainability.

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R 2. Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

The screening and monitoring of residual antibiotics in pork meat were conducted in Ho Chi Minh City, Vietnam, from 2014 to 2015. One hundred samples collected from a wholesale market were screened on-site using a microbiological kit, and eighteen samples were determined as positive for residual antibiotics. Once transferred to the lab, all the samples were analyzed for 28 antibiotics using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Sulfamethazine residues (11–1600 μg/kg) were detected in 23 samples by LC-MS/MS. Notably, 11 samples with sulfamethazine concentrations ranging from 310 to 1600 μg/kg were detected as “positive” in the initial screening, whereas 12 other samples with sulfamethazine levels below 200 μg/kg were screened as “negative”. No other targeted antibiotics were identified among the remaining positive samples. These results show the potential and limitations of a monitoring system combining on-site microbiological tests and an LC-MS/MS method.

Satellite remote-sensing products with high spatial and time resolution are expected to provide alternative data sources for data-sparse regions. This study clarifies if the satellite–gauge product outperforms the satellite-only product by comparing remote-sensing precipitation products: one that incorporates rain gauge data (GSMaP-Gauge) and one that uses satellite only (GSMaP-MVK). The appropriateness of those two commonly used high-resolution products as the input to the conceptual hydrological model Hydrologiska Byråns Vattenbalansavdelning for stream flow prediction was also investigated. In addition, we also analyzed the deviations of model parameters due to the bias in remote-sensing precipitation inputs compared to standard ground measurements. The results indicated that GSMaP-Gauge was superior, with satisfactory to good performances in predicting stream flow in both temperate and subtropical basins (Hyeonsan, Fuji, and Da). However, its performance was slightly worse than GSMaP-MVK in the Upper-Cau basin, which was explained by the poor quality of the adjusted data source due to sparse data and the satellite–gauge blending algorithm of GSMaP-Gauge. Better parameter agreements with the observations of GSMaP-Gauge than GSMaP-MVK were found in the Hyeonsan and Da river basins where GSMaP-Gauge showed almost consistent relationship of monthly rainfall compared to ground measurements.

Antibiotic residues in aquatic products in Vietnam were investigated. A total of 511 fish and shrimp samples were collected from markets in Ho Chi Minh City (HCMC), Thai Binh (TB), and Nha Trang (NT) from July 2013 to October 2015. The samples were extracted with 2% formic acid in acetonitrile and washed with dispersive C18 sorbent. Thirty-two antibiotics were analyzed by LC-MS/MS. Of the 362 samples from HCMC, antibiotic residues were found in 53 samples. Enrofloxacin was commonly detected, at a rate of 10.8%. In contrast, samples from TB and NT were less contaminated: only 1 of 118 analyzed samples showed residues in TB and only 1 of 31 showed residues in NT. These differences were attributed to the local manufacturing/distribution systems. To understand the current status of antibiotic use and prevent adverse effects that may be caused by their overuse, continual monitoring is required.

In this study, the main objective is to develop and compare several advanced Artificial Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized with Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the prediction of daily rainfall in Hoa Binh province, Vietnam. For this, meteorological variable parameters such as maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation were collected and used as input parameters and daily rainfall as an output parameter in the models. Validation of the developed models was achieved using various quality assessment criteria such as correlation coefficient (R) and Mean Absolute Error (MAE), Skill Score (SS), Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ratio (FAR). The results showed that all the AI models provided reasonable predictions of daily rainfall but the SVM was found to be the best method for predicting rainfall. This method was also found to be the most robust and efficient prediction model while taking into account of input variability using the Monte Carlo approach. This AI based study would be helpful in quick and accurate prediction of daily rainfall.

There is mounting concern about how to support decision-makers in driving sustainable water resources management; science needs to support the decision-making process to promote evidence-based decisions. To this end, sustainability assessment is considered a useful technique, which provides enough information to assist management. This study proposed a groundwater sustainability assessment framework, which is developed from a regular sustainability assessment approach and analytical hierarchy process (AHP). In the proposed framework, the three main pillars (environmental, social, and economic) of the concept of sustainability were considered the three important sustainability criteria. Hence, we demonstrated the proposed framework for a Hanoi case study with focus on the environmental sustainability criterion. The concept of AHP was used to create the main sustainability components (the three criteria, associated with their aspects and indicators) of a hierarchy, which appropriately cover environmental sustainability issues of groundwater resources in the target area. Based on the available reliable data of the current problems in Hanoi, we proposed three main sustainability aspects (quantity, quality, and management) and, accordingly, selected their twelve environmental sustainability indicators. To determine a reasonable sustainability assessment, we considered a conventional linear and non-linear relationship between the indicators and the corresponding sustainability indices. As for the results from the Hanoi case study, the environmental sustainability indices obtained from using a combined linear and non-linear relationship case appropriately reflect the current situation, that is, the environmental sustainability assessment is close to reality. The sustainability indices of the quantity, quality, and management aspects of groundwater were appropriately assessed at acceptable levels, resulting in Hanoi being rated at the acceptable level in the final environmental sustainability index. The variability of the environmental sustainability indices indicated that the current groundwater abstraction networks are heavily concentrated in a few specific areas in Hanoi, which is not optimal for utilizing the rich natural recharge resources of the area. Improvement of the current poor groundwater quality and strict enforcement of environmental regulations are essential to enhancing the environmental sustainability and, more importantly, to drive Hanoi towards sustainable groundwater resources.

This chapter presents a hybrid intelligent model for time-series modeling and forecasting horizontal displacement of hydropower dams, named as ABC-LSSVR. In the proposed hybrid approach, least-squares support vector regression (LSSVR) was used to create the displacement model. Furthermore, the model was optimized using the Artificial Bee Colony (ABC) algorithm. The time-series monitoring data at the Hoa Binh hydropower dam of Vietnam were used as a case study. Accordingly, reservoir water lever, air temperature, ageing, and observed horizontal displacement, which are time-series monitoring data, were used to establish and verify the model. The experiment result showed that the ABC-LSSVR model has high accuracy with both the training and forecasting data and the ABC algorithm is capable of finding optimized parameters for the model autonomously. The effectiveness of the model is further assessed by comparing with results produced from two benchmark models, the support vector regression and the multilayer perceptron neural networks. Since the ABC-LSSVR model outperforms the two benchmark models, we concluded that the ABC-LSSVR model is a promising alternative tool that is highly recommended for modeling and forecasting the horizontal displacement of hydropower dams.

CO2 emission from burning coal has been used as a carbon source for growing Cyanobacterium Spirulina platensis in order to minimize the cost of biomass production, and currently to carry out CO2 bioremediation. This article presents the results of feeding S. platensis in laboratory conditions with 2 formulas including Pure CO2 and Flue gas CO2 upon using modified Zarrouk’s medium with 1.6 g / L NaHCO3 and 2g / L Na2CO3. Pure CO2 with 1.2% concentrations taken from 99% vol of industrial CO2 and CO2 gas (1.2%) received from the flue gas through the Modular system of Exhausted Gas Treatment (MEGT). Growth of the Cyanobacterium using CO2 - Flue gas is equivalent to CO2 -Pure. On this basis, S. platensis has been cultivated outdoor in an 25 m2 pond using CO2 gas (1.2%) from the tunnel brick factory emissions after suitable cleaning. The experiment in an outdoor pond system of 25 m2 indicated that the yield of biomass is of 10g/m2d with high-protein content (62.58 ± 2.34%) and fatty acids of high nutritional value (8.72 ± 0.14%), such as Omega - 6 and Omega - 3 reaching 14.74 ± 0.42% and 26.05 ± 0.64% of total fatty acid content, respectively. The quality of Spirulina cultured by CO2 gas meets the requirements for functional foods according to Vietnam national food standards. The article also presents the results of biomass productivity and chemical composition of the Cyanobacterium in different culture conditions.