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AbstractThe objective of this study was to investigate a hybrid framework for Industry 4.0 and a circular economy for logistics and supply chains with an environmentally‐friendly and sustainable basis. Data were collected from universities, companies, and civil service departments. Partial Least Squares Structural Equation Modeling was applied to evaluate and validate hypotheses. Key findings comprise (a) confirmation of a predominant direct relationship between Industry 4.0 and adopting a circular economy for green logistics and a sustainable supply chain, which stimulates strategic change; and (b) deliberation about current strategic awareness and operations occurs, giving rise to the promulgation and implementation of state authority policies, and advancements in modern logistics and supply chain programs at universities. Since the analysis concentrated on logistical and supply chain matters, investigation of other variables would be required for other industrial activities such as manufacturing.

Abstract. The Mekong Delta is the most important food production area in Vietnam, but salinity intrusion during the dry season poses a serious threat to agricultural production and livelihoods. A seasonal forecast of salinity intrusion is required in order to mitigate the negative effects. This communication presents a statistical seasonal forecast model based on logistic regression using either the ENSO34 index or streamflow as a predictor. The model is able to reliably predict the salinity intrusion up to 9 months ahead (receiver operating characteristic (ROC) scores: >0.8). The model can thus be used operationally as a basis for timely adaptation and mitigation planning.

Front Cover -- The Mekong River Basin -- The Mekong River Basin: ECOHYDROLOGICAL COMPLEXITY FROM CATCHMENT TO COAST -- Copyright -- Contents -- Contributors -- About the editors -- Foreword -- Preface -- Introduction chapter -- 1. Eco-hydrology-a new approach to sustainable river basin management -- 2. Eco-hydrological issues in Mekong River Basin -- 3. The eco-hydrological frameworks applied in the book -- 4. Book chapters outline and storyline -- References -- Further reading -- Section I -- 1 - Hydroclimate variability and change over the Mekong River Basin -- 1.1 Introduction -- 1.2 Data and methods -- 1.2.1 River discharge data -- 1.2.2 Hydroclimate data -- 1.2.3 Climate variability indices -- 1.2.4 Other data -- 1.2.5 Basin rainwater, runoff -- 1.2.6 Analysis details -- 1.3 The normal regional hydroclimate -- 1.4 Tropical cyclones -- 1.5 Changing climate and decadal trends -- 1.5.1 Precipitation normals -- 1.5.2 Air temperature normals -- 1.6 Impact of ENSO and low-frequency modes in the Mekong hydroclimate -- 1.6.1 El Niño-southern oscillation -- 1.6.2 Decadal-to-multidecadal variability and trends -- 1.7 The Mekong River Basin water budget -- 1.7.1 Climatology -- 1.7.2 Variability and recent trends -- 1.8 The Mekong streamflow and a basin-integrated model -- 1.8.1 Streamflow climatology -- 1.8.2 Summer streamflow anomalies -- 1.9 Summary -- References -- 2 - Hydrology and water resources in the Mekong River Basin -- 2.1 Introduction -- 2.2 Motivation -- 2.3 Mekong hydrology -- 2.3.1 Observational, remote-sensing, and modeling approaches to estimate water budget -- 2.3.2 Impacts of land use/land cover changes and climate change -- 2.3.3 Irrigation and water resources -- 2.3.4 Sediment, salinity, and nutrients -- 2.3.5 Hydrologic extremes -- 2.4 Challenges in eco/socio hydrology perspectives and solutions.

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naï ; ve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015&ndash ; 2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.

Abstract. Hydro-numerical models are increasingly important to determine the adequacy and evaluate the effectiveness of potential flood protection measures. However, a significant obstacle in setting up hydro-numerical and associated flood damage models is the tedious and oftentimes prohibitively costly process of acquiring reliable input data, which particularly applies to coastal megacities in developing countries and emerging economies. To help alleviate this problem, this paper explores the usability and reliability of flood models built on open-access data in regions where highly resolved (geo)data are either unavailable or difficult to access yet where knowledge about elements at risk is crucial for mitigation planning. The example of Ho Chi Minh City, Vietnam, is taken to describe a comprehensive but generic methodology for obtaining, processing and applying the required open-access data. The overarching goal of this study is to produce preliminary flood hazard maps that provide first insights into potential flooding hotspots demanding closer attention in subsequent, more detailed risk analyses. As a key novelty, a normalized flood severity index (INFS), which combines flood depth and duration, is proposed to deliver key information in a preliminary flood hazard assessment. This index serves as an indicator that further narrows down the focus to areas where flood hazard is significant. Our approach is validated by a comparison with more than 300 flood samples locally observed during three heavy-rain events in 2010 and 2012 which correspond to INFS-based inundation hotspots in over 73 % of all cases. These findings corroborate the high potential of open-access data in hydro-numerical modeling and the robustness of the newly introduced flood severity index, which may significantly enhance the interpretation and trustworthiness of risk assessments in the future. The proposed approach and developed indicators are generic and may be replicated and adopted in other coastal megacities around the globe.

Based on a qualitative case study in An Giang province, Vietnam, we mapped the understanding of the 'Living with Floods' (LWF) concept and the implementation of three projects to explain the effectiveness of water governance in Vietnam. We have demonstrated how perceptions on the LWF concept differ per government level and the limits of water governance effectiveness. Diverging perceptions undermine the effectiveness of water governance. A framework and a list of indicators are proposed to measure the effectiveness of floodwater governance. Integrating local and social aspects in LWF policies and vertical coordination may help align short-term benefits with long-term adaptation.

Abstract. Urban flooding is a major challenge for many megacities
in low-elevation coastal zones (LECZs), especially in Southeast Asia. In
these regions, the effects of environmental stressors overlap with rapid
urbanization, which significantly aggravates the hazard potential. Ho Chi
Minh City (HCMC) in southern Vietnam is a prime example of this set of
problems and therefore a suitable case study to apply the concept of
low-regret disaster risk adaptation as defined by the Intergovernmental
Panel on Climate Change (IPCC). In order to explore and evaluate potential
options of hazard mitigation, a hydro-numerical model was employed to
scrutinize the effectiveness of two adaptation strategies: (1) a classic
flood protection scheme including a large-scale ring dike as currently
constructed in HCMC and (2) the widespread installation of small-scale
rainwater detention as envisioned in the framework of the Chinese Sponge
City Program (SCP). A third adaptation scenario (3) assesses the combination of both approaches (1) and (2). From a hydrological point of view, the reduction in various flood intensity
proxies that were computed within this study suggests that large-scale flood protection is comparable but slightly more effective than small-scale
rainwater storage: for instance, the two adaptation options could reduce the normalized flood severity index (INFS), which is a measure combining flood depth and duration, by 17.9 % and 17.7 %, respectively. The
number of flood-prone manufacturing firms that would be protected after
adaptation, in turn, is nearly 2 times higher for the ring dike than for
the Sponge City approach. However, the numerical results also reveal that
both response options can be implemented in parallel, not only without
reducing their individual effectiveness but also complementarily with
considerable added value. Additionally, from a governance perspective,
decentralized rainwater storage conforms ideally to the low-regret paradigm:
while the existing large-scale ring dike depends on a binary commitment (to
build or not to build), decentralized small- and micro-scale solutions can
be implemented gradually (for example through targeted subsidies) and add
technical redundancy to the overall system. In the end, both strategies are
highly complementary in their spatial and temporal reduction in flood
intensity. Local decision-makers may hence specifically seek combined
strategies, adding to singular approaches, and design multi-faceted
adaptation pathways in order to successfully prepare for a deeply uncertain
future.

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality. ; Published version

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality. ; Published version