Suchergebnisse

Der Verfasser gibt zunächst einen Überblick über die sozioökonomische und politische Struktur der kambodschanischen Gesellschaft. Er behandelt im folgenden die strategische Bedeutung Südostasiens, die Interessen der Supermächte in dieser Region sowie den Einfluß dieser Interessen auf die kambodschanische Politik. Vor diesem Hintergrund werden die Konsequenzen der Pol-Pot-Diktatur für Kambodscha dargestellt. Es schließt sich eine Untersuchung der Strategien Chinas und Vietnams in bezug auf Kambodscha und ihre Einflußnahme auf politische Gruppierungen in Kambodscha an. Abschließend wird die Rolle der Bewegung der Blockfreien im Kambodscha-Konflikt untersucht. (BIOst-Wpt)

PurposeOver the past 50 years India has been experiencing rapid population growth, causing the migration of a large part of the population to the cities looking for livelihood. This resulted in massive increments of population in the cities that has led to the increase of pollution. Gujarat, being a highly industrialized state, is a case in point. The systems for treatment and water disposal of this state are highly challenged. The north‐western state of Gujarat has no effective systems for treatment or disposal of waste water. The purpose of this article is to address this problem, introducing a geographic information system (GIS) approach to record the characterization, analyze the needs and generate a conceptual GIS database in the state.Design/methodology/approachThis paper outlines the background, suggested methodology for the development of a GIS database pollution dependent control of water pollution in the state of Gujarat in India. The present research is to install a document management system that has been developed in providing organizing chart, sorting, querying and retrieving of key data. A computerized laboratory information system on monitoring of quality of ambient air has been developed.FindingsAn integrated GIS database has been generated involving creation of pollutant contours, querying and visualizing the query output in spatial and non‐spatial form.Originality/valueThe authors have created a complete geo‐spatial database for the environmental monitoring for the whole state of Gujarat. They have dealt with nearly 36,000 different files from different sources and put them together to create the database. A computerized laboratory information system on monitoring of quality of ambient air has been developed. Front‐end application programs have been developed in Visual Basic and the back‐end database to integrate the laboratory data and the existing data in oracle database.

Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data.

© 2019 The Author(s). Background: Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence. Methods: This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models. Results: Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R2 (0.85, 0.80, 0.78 and 0.75) are used. Conclusion: Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.

© 2019 Elsevier Ltd The rapid urbanization and environmental imbalance have significantly challenged Pakistan's organizational capacity to respond and initiate relief efforts and hence increasing its vulnerability to flood disaster situations. This study considers systems thinking approaches such as, Causal Loop Diagram (CLD) and Driver-Pressures-States-Impacts-Responses (DPSIR) framework to identify key stakeholders to disaster risk reduction and analyze various social, technical, institutional, cultural, infrastructural and environmental factors that contribute to flooding in Pakistan. Based on the information collected through expert interviews with key government officials and analyzing the existing literature and research reports on floods and disaster management, policy recommendations for long-term flood disaster response strategies have been made. The comprehensive set of recommendations towards effective flood management and mitigation would help build resilience from floods by raising community awareness and enhancing institutional capacities at federal, provincial and district government levels in the countries like Pakistan and other developing nations facing catastrophic flood situations.

Geomophological hazard assessment is an important component of natural hazard risk assessment. This paper presents GIS-based geomorphological hazard mapping in the Red Sea area between Safaga and Quseir, Egypt. This includes the integration of published geological, geomorphological, and other data into GIS, and generation of new map products, combining governmental concerns and legal restrictions. Detailed geomorphological hazard maps for flooding zones and earth movement potential, especially along the roads and railways, have been prepared. Further the paper illustrates the application of vulnerability maps dealing with the effect of hazard on urban areas, tourist villages, industrial facilities, quarries, and road networks. These maps can help to initiate appropriate measures to mitigate the probable hazards in the area.

Abstract. Geomophological hazard assessment is an important component of natural hazard risk assessment. This paper presents GIS-based geomorphological hazard mapping in the Red Sea area between Safaga and Quseir, Egypt. This includes the integration of published geological, geomorphological, and other data into GIS, and generation of new map products, combining governmental concerns and legal restrictions. Detailed geomorphological hazard maps for flooding zones and earth movement potential, especially along the roads and railways, have been prepared. Further the paper illustrates the application of vulnerability maps dealing with the effect of hazard on urban areas, tourist villages, industrial facilities, quarries, and road networks. These maps can help to initiate appropriate measures to mitigate the probable hazards in the area.

Geomophological hazard assessment is an important component of natural hazard risk assessment. This paper presents GIS-based geomorphological hazard mapping in the Red Sea area between Safaga and Quseir, Egypt. This includes the integration of published geological, geomorphological, and other data into GIS, and generation of new map products, combining governmental concerns and legal restrictions. Detailed geomorphological hazard maps for flooding zones and earth movement potential, especially along the roads and railways, have been prepared. Further the paper illustrates the application of vulnerability maps dealing with the effect of hazard on urban areas, tourist villages, industrial facilities, quarries, and road networks. These maps can help to initiate appropriate measures to mitigate the probable hazards in the area.

© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.

© 2019 by the authors. Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods-Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance-to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

© 2019 by the authors. Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.

© 2019 by the authors. This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely "AB-ADTree", for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including singleADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB-ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.