Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Tradition and Innovation in Construction Project Management

This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

In the aftermath of the financial crisis, supervisory authorities have considerably improved their approaches in performing financial stress testing. However, they have received significant criticism by the market participants due to the methodological assumptions and simplifications employed, which are considered as not accurately reflecting real conditions. First and foremost, current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models, making their integration a really challenging task with significant estimation errors. Secondly, they still suffer from not employing advanced statistical techniques, like machine learning, which capture better the nonlinear nature of adverse shocks. Finally, the static balance sheet assumption, that is often employed, implies that the management of a bank passively monitors the realization of the adverse scenario, but does nothing to mitigate its impact. To address the above mentioned criticism, we introduce in this study a novel approach utilizing deep learning approach for dynamic balance sheet stress testing. Experimental results give strong evidence that deep learning applied in big financial/supervisory datasets create a state of the art paradigm, which is capable of simulating real world scenarios in a more efficient way.Comment: Preprint submitted to Journal of Forecastin

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Modeling the relationship between air quality and intelligent transportation system (ITS) with artificial neural networks.

Environmental or air quality impacts of Intelligent Transportation Systems (ITS) are very difficult to measure. Some researchers have attempted to quantify the effects of individual ITS application on emissions; yet, the effects of ITS as a whole on ambient air quality have not been investigated. The objective of this research was to model the relationship between ITS and ambient air quality. The multiple Artificial Neural Networks (ANN) training with the data yielded a model for predicting the air quality. In addition, the ANN made the measurement of the effect of ITS on air quality possible. Data pertaining to sixty US cities (urbanized area) were used for this research. Input variables used were related to transportation and local characteristics, and ITS applications. Output variables were the annual average concentrations of CO, Ozone, and N02 in ambient air. The K-fold cross validation technique was used to train the ANN. The results of ANN model were compared with that of a Multiple Regression (MR) model showing the supremacy of ANN over MR. The ANN model results show that the Mean Absolute Errors (MAEs) in prediction vary from 5 to 20 %. This variance is justified since the factors related with industries, which contribute significantly to air pollution, have not been taken into consideration in this study. There were some unusual findings: in contrast to the common assumptions, N02 concentration increases with ITS intensity, and Ground Level Ozone concentration, in ambient air, seemed to be more transportation-dependent as compared with that of CO and N02• A recommendation for further research on this topic is to include more input variables, especially those which are relatcd with industries, to improve the accuracy of prediction. Scientific experimentations have also been recommended to corroborate the unusual findings

Analysing Financial Distress in Malaysian Islamic Banks: Exploring Integrative Predictive Methods

Against the background of global financial crisis, some argue in favour of the ‘resilience’ of Islamic finance, while others suggest that Islamic financial institutions are not more prone to distress and crisis than their conventional counterparts. However, there have been a number of cases of Islamic finance and banking distress in recent years, including instances in Malaysia. These cases, hence, motivated this study in terms of emphasising the importance of employing financial distress prediction models for analysing Islamic banks. This study aims at empirically exploring, examining and analysing the financial distress of the Malaysian Islamic banks. In doing so, the effectiveness of the existing early warning statistical insolvency prediction models that have been used in previous studies, and a particular model adapted by Islamic banks in Malaysia were critically evaluated. This study, hence, employed a number of models to predict the financial distress faced by Islamic banks in Malaysia. In addition, an attempt was made at the modification of the existing early warning insolvency prediction models in evaluating and analysing the financial distress of Malaysian Islamic banks. This research is constructed within four empirical chapters by employing three prediction models in assessing the financial distress of Islamic banks. The first empirical chapter analyses the secondary data collected from a sample of Islamic banks, based on selected ratios developed in the literature, whereby a comprehensive description of these selected financial ratios in terms of descriptive statistical analysis for the selected Islamic banks in Malaysia is provided. The second empirical chapter investigates the performance of the ‘emerging market Z-score’, introduced by Altman in predicting the performance of Islamic banks and conventional banks in Malaysia. The study aimed to introduce the EM Z-score as a valuable analytical tool in monitoring the deterioration of the performance of banks as well as looking at the impact of the global financial crisis on the performance of Islamic and conventional banks. This chapter examines thirteen Islamic banks and ten conventional banks during the period of 2005-2010. The results show that the EM Z-score for all banks is well above the cut-off point of 2.6, although for Islamic banks the EM Z-score showed a declining trend whilst for conventional banks it showed an increasing trend. This empirical evidence is important for the banks since it provides a warning signal to the banks’ management as well as the related parties involved in the planning, controlling and decision making process. The third empirical chapter presents the newly constructed integrated predictive model designed to evaluate and analyse the financial distress of Islamic banks in Malaysia, which can be used as an alternative model for regulators in monitoring the performance of Islamic banks that are experiencing any serious financial problems. This paper develops a preliminary model for the prediction of the performance level of Islamic financial institutions for the period of December 2005 to September 2010 by using quarterly data for ten selected Islamic banks in Malaysia. For this, factor analysis and three parametric models (discriminant analysis, logit analysis and probit analysis) are used. The results depict that the first few quarters before the benchmark quarter are the most important period for making a correct prediction and crucial decisions on the survival of Islamic banks. Thus, the results demonstrate the predictive ability of the integrated model to differentiate between the healthy and non-healthy Islamic banks, therefore reducing the expected cost of bank failure. The fourth empirical chapter conducts further exploration in predicting the financial distress position of Islamic banks by introducing new variables such as the funding structure, deposit composition, and macroeconomic variables. Using the same sample and data set for Islamic banks as in the previous chapter, this study shows the relationship between the banks’ funding profiles and other alternative variables, and the Islamic banks’ performance in Malaysia. For this, the logit model is used. Based on the results of all models, this study recommended two final models, which showed an excellent fit for predicting the Islamic banks’ performance. The results indicate that none of the macroeconomic variables included were significant, thus suggesting that the performance of Islamic banks in Malaysia was not affected by the economic conditions throughout the study period. This can perhaps be attributed to efficient regulation and supervision by the relevant authorities in the country

Three essays on the use of neural networks for financial prediction

The number of studies trying to explain the causes and consequences of the economic and financial crises usually rises considerably after a banking crisis occurs. The dramatic effects of the most recent financial crisis on the real economy around the world call for a better comprehension of previous crises as a way to anticipate future crisis episodes. It is precisely this objective, preventing future crises, the main motivation of this PhD dissertation. We identify two important mechanisms that have failed during the latest years and that are closely related to the onset of the financial crisis: The assessment of the solvency of banks along with the systemic risk over the time, and the detection of the macroeconomic imbalances in some countries, especially in Europe, which made the financial crisis evolve through a sovereign crisis. Our dissertation is made up of three different essays, trying to go a step ahead in the knowledge of these mechanisms.Departamento de Economía Financiera y ContabilidadDoctorado en Economía de la Empres

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security