A holographic memory approach for pollution forecasting in a high-density urban environment

In this work, the Holographic Associative Memory (HAM) paradigm was used as the core of a forecasting software tool for benzopyrene estimations near a highly populated zone. The presented tool was trained with data coming from a monitoring station near a steel plant in Genova, Italy. The decoding of test stimuli was performed with two different methods, the holographic complex number technique (HCD) and the closest holographic neighbor decoding (CHN). The cost–performance relation of both methods is outlined and compared. The atmospheric scenarios used for modeling benzopyrene behavior contained meteorological and chemical variables correlated to the formation and dispersion of such contaminant. The obtained results show an accurate performance of the HAM method either for identifying the main features involved in benzopyrene estimation and for the forecasting itself. Finally, some concluding remarks regarding the performance of both decoding methods are presented.     

Normalized difference haze index: a new spectral index for monitoring urban air pollution

Haze is an undesirable meteorological and environmental phenomenon that can cause enormous harm to the environment, people's lives and health, and economic activities. This study focuses on Nanjing, Yangzhou and Suzhou in the lower reaches of the Yangtze River valley, China, which have suffered from the adverse effects of hazy weather in recent years. The spectral influence of haze on surface features was determined through analysis of the spectral variations of surface covers between hazy and haze-free days. On the basis of the established relationship, a new index called the normalized difference haze index (NDHI) was derived using moderate resolution imaging spectroradiometer (MODIS) data from winter 2008–2009. Correlation analysis of the derived NDHI with in situ observed PM₁₀ (particulate matter with diameter <10 μm) data reveals that NDHI over water bodies has a coefficient of 0.74, 0.57 and 0.67 with PM₁₀ for Nanjing, Yangzhou and Suzhou, respectively. It is concluded that NDHI is a reliable indicator of air pollution. It can be used as a new method of effectively monitoring air pollution from remotely sensed data.     

A software tool for the creation of a typical meteorological year

The generation of a typical meteorological year is of great importance for passive solar architectural applications. In this context, within the PASCOOL project, a software tool has been developed, utilizing the Filkenstein-Schafer statistical method for the creation of a typical meteorological year. Using this software tool, a typical meteorological year was generated for Athens, Greece. The data used were from the National Observatory of Athens and cover a period of 17 years (1977–1993).     

FuSSFFra,a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens

Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.

     

Influences of urban forms on traffic-induced noise and air pollution: Results from a modelling system

Based on the work of Jensen [Jensen, S.S., 1998. Mapping human exposure to traffic air pollution using GIS. Journal of Hazardous Materials 61(1–3), 385–392; Jensen, S.S., 1999. A geographic approach to modelling human exposure to traffic air pollution using GIS. Ph.D. Thesis. National Environmental Research Institute, Roskilde], a prototype system for modelling noise and air pollution is developed for the Macao Peninsula. The system integrates a road traffic noise model, an operational air pollution model, digital maps, an urban landscape model and a Geographic Information System (GIS). Compared with mesoscale model systems with input/output resolution in kilometres, the present one has a higher spatial resolution down to individual buildings along both sides of the street. Applying the developed model system, a preliminary study investigates the ways that four urban forms existing nowadays on the Macao Peninsula influence vehicle transport and street environment. This paper shows that the urban forms in historical areas with narrower roads, complex road networks and a higher density of intersections lead to lower traffic volumes and thus lower noise pollution. However, the greater street canyon effects in these historical urban areas lead to higher carbon monoxide (CO) concentrations.     

Performance evaluation of competing forecasting models: A multidimensional framework based on MCDA

So far, competing forecasting models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria – a situation where one cannot make an informed decision as to which model performs best overall; that is, taking all performance criteria into account. To overcome this methodological problem, we propose to use a Multi-Criteria Decision Analysis (MCDA) based framework and discuss how one might adapt it to address the problem of relative performance evaluation of competing forecasting models. Three outranking methods have been used in our empirical experiments to rank order competing forecasting models of crude oil prices; namely, ELECTRE III, PROMETHEE I, and PROMETHEE II. Our empirical results reveal that the multidimensional framework provides a valuable tool to apprehend the true nature of the relative performance of competing forecasting models. In addition, as far as the evaluation of the relative performance of the forecasting models considered in this study is concerned, the rankings of the best and the worst performing models do not seem to be sensitive to the choice of importance weights or outranking methods, which suggest that the ranks of these models are robust.     

Performance evaluation of weights selection schemes for linear combination of multiple forecasts

Time series modeling and forecasting are essential in many domains of science and engineering. Extensive works in literature suggest that combining outputs of different forecasting methods substantially increases the overall accuracies as well as reduces the risk of model selection. The most popular method of forecasts combination is the weighted averaging of the constituent forecasts. The effectiveness of this method solely depends on appropriate selection of the combining weights. In this paper, we comprehensively evaluate a wide variety of benchmark weights selection techniques for linear combination of multiple forecasts in terms of their prediction accuracies. Nine real-world time series from different domains and five individual forecasting methods are used in our empirical work. A robust scheme is also suggested for fairly ranking the combination methods on the basis of their forecasting performances. Our study precisely demonstrates the relative strengths and weaknesses of various benchmark linear combination techniques which evidently can be of much practical importance.     

城市空气污染脆弱性评价模型及实证研究

利用中国74个样本城市的微观监测数据,通过构建概率模型和分解模型,以三阶段可行广义最小二乘法估计的参数测度城市空气污染脆弱性.研究发现:超过80%的样本城市具有空气污染脆弱性,且呈地域性特征;脆弱性不仅在相邻两级之间转移,还存在跨级间突变.空气污染的差异性表现为近20%的样本城市中度及以上空气污染可能性较高, 46%的城市发生空气轻度污染是大概率事件,仅有12%的城市无污染脆弱性;而由于"污染避难所"效应的存在,空气污染"热点"城市趋向于欠发达地区.根据实证结果,对城市空气污染程度分类并提出了差异化的应对措施,以期为有关部门空气治理政策的制定提供科学依据,从而达到突出重点、分类指导、多管齐下、科学施策的目的.     

Simulation and evaluation of urban bus-networks using a multiagent approach

Evolution of public road transportation systems requires analysis and planning tools to improve service quality. A wide range of road transportation simulation tools exist with a variety of applications in planning, training and demonstration. However, few simulation models take into account traveler behaviors and vehicle operation specific to public transportation. We present in this paper a bus-network simulation tools which include these specificities and allows to analyze and evaluate a bus-network at diverse space and time scales. We adopt a multiagent approach to describe the global system operation as behaviors of numerous autonomous entities such as buses and travelers.     

Improving the disaggregation of MODIS land surface temperatures in an urban environment: a statistical downscaling approach using high-resolution emissivity

Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information; thus, the downscaling of LST is recognized as being an important and inevitable intermediate process. In this paper, improvement in the downscaled LST accuracy is investigated, employing the statistical downscaling methodology in an urban setting. A new approach is proposed, where thermal radiances are disaggregated using multiple regression analysis and are then combined with emissivity values derived from a high-resolution image classification. Predictors include reflectance values, built-up and vegetation indices, and topographic data. Surface classification is performed utilizing machine learning techniques and fusing Sentinel-2 imagery with ancillary data. Thermal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor are downscaled from their original resolution to 100 m in the city of Athens, Greece. Validation of sharpened temperatures is performed using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface temperature product and in-situ measurements. It is demonstrated that the proposed downscaling framework using ridge regression has the potential to produce reliable, high temporal LST estimates with an average error of fewer than 2 K, while consistently having a better accuracy than the reference, single-predictor downscaling of the MODIS LST product.     

Improved parameterisation for the numerical modelling of air pollution within an urban street canyon

Numerical modelling for application to wind flow and dispersion in urban environments has noticeably progressed in recent years, to currently represent a widely used tool for simulating mechanical processes governing air pollution in complex geometries. In particular, Computational Fluid Dynamic (CFD) techniques based on RANS (Reynolds-Averaged Navier–Stokes equations) models, are extensively used to produce detailed simulations of the wind flow and turbulence in the urban canopy. However, several studies have indicated that RANS models, and in particular the widely used standard k–? turbulence model, are sensitive to the particular form of inlet profiles for turbulence and velocity. In the present study, simulations of the wind flow and dispersion within an idealised street canyon were carried out using the standard k–? turbulence model provided by the commercial software FLUENT. The aim of this study was to improve the standard k–? model performance by modifying the model parameters according to the chosen form of inlet profiles for velocity and turbulence. Capability of the model to reproduce real wind flow fields, turbulence and concentration patterns was evaluated by comparing the model results against recently published wind tunnel data. Results for turbulent kinetic energy and concentration showed that the redefinition of the default dispersive parameters can significantly enhance the model performance. The newly proposed parameterisations of the standard k–? turbulence model can be readily implemented within commercial CFD software packages, offering a reliable modelling tool for application to urban air pollution and other environmental studies.     

A grey approach for forecasting in a supply chain during intermittentdisruptions

The supply chains today have become vulnerable to frequent disruptions, and with continuing emphasis on efficiency, lacks robustness to deal with them. A part of the solution lies in forecasting the disruption beforehand and the other part in knowing which policies will suit such disrupted conditions best. Accurate and immediate forecasts are a must in a supply chain and hence play a huge role in stabilizing. This study compares the performance of three established forecasting methods (moving average, weighted moving average and exponential smoothing) as well as grey prediction method, during disruptions and stable situations. The experiments are performed in the form of discrete event simulation, on a four stage beer game settings. The results show that moving average and weighted moving average methods become incompetent during disruptions, and are useful only during stable times, when the demand hovers around a predefined mean value. Exponential smoothing and grey method seems to give better results during disruptions and also during stable times in upstream tiers. Grey prediction method in particular is the best method when the disruption frequency is high and also when the disruption impact is gradual rather than sudden.     

Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting

A versatile data assimilation scheme for remote sensing snow cover products and meteorological data was developed, aimed at operational use for short-term runoff forecasting. Spatial and temporal homogenisation of the various input data sets is carried out, including meteorological point measurements from stations, numerical weather predictions, and snow maps from satellites. The meteorological data are downscaled to match the scale of the snow products, derived from optical satellite images of MODIS and from radar images of Envisat ASAR. Snow maps from SAR and optical imagery reveal systematic differences which need to be compensated for use in snowmelt models. We applied a semi-distributed model to demonstrate the use of satellite snow cover data for short-term runoff forecasting. During the snowmelt periods 2005 and 2006 daily runoff forecasts were made for the drainage basin Ötztal (Austrian Alps) for time lags up to 6 days. Because satellite images were obtained intermittently, prognostic equations were applied to predict the daily snow cover extent for model update. Runoff forecasting uncertainty is estimated by using not only deterministic meteorological predictions as input, but also 51 ensemble predictions of the EPS system of the European Centre for Medium Range Weather Forecast. This is particularly important for water management tasks, because meteorological forecasts are the main error source for runoff prediction, as confirmed by simulation studies with modified input data from the various sources. Evaluation of the runoff forecasts reveals good agreement with the measurements, confirming the usefulness of the selected data processing and assimilation scheme for operational use.     

A methodological approach for spatial downscaling of TRMM precipitation data in North China

Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25° to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25°, 0.50°, 0.75°, 1.00°, and 1.25°). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25°; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R² = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R² = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).     

A framework for linking urban form and air quality

The importance of urban form in the quest for sustainable development has been recognised in a number of countries in recent years. However, there has been limited progress in bringing environmental planning into the sphere of urban systems planning. This situation can be largely attributed to the absence of advanced integrated land use– transport–environment modelling tools capable of analysing the behaviour of complex, dynamic systems. This paper describes an initial attempt to develop a framework for integrating land use, transport and airshed models for evaluating the effect of city form on air quality. The framework identifies the relationship between various components such as the GIS database, the land use–transport–environment module and the airshed model. Issues concerning the structure and robustness of the framework are discussed and results from a recent air quality inquiry are presented.     

A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studies

Model developments to assess different air pollution exposures within cities are still a key challenge in environmental epidemiology. Background air pollution is a long-term resident and low-level concentration pollution difficult to quantify, and to which population is chronically exposed. In this study, hourly time series of four key air pollutants were analysed using Hidden Markov Models to estimate the exposure to background pollution in Madrid, from 2001 to 2017. Using these estimates, its spatial distribution was later analysed after combining the interpolation results of ordinary kriging and inverse distance weighting. The ratio of ambient to background pollution differs according to the pollutant studied but is estimated to be on average about six to one. This methodology is proposed not only to describe the temporal and spatial variability of this complex exposure, but also to be used as input in new modelling approaches of air pollution in urban areas.     

Quantitative mapping of air pollution density using Earth observations: a new processing method and application to an urban area

Urban air pollution can be quantified in terms of atmospheric turbidity if satellite images of similar geometry acquired under clear atmosphere and pollution conditions are radiometrically compared. This comparison can be conveniently carried out by evaluating 'the apparent contrast reduction' due to aerosols over land. However, this method is subject to misclassification due to ground-reflectance temporal variations. This paper proposes the complementary application of 'the temperature attenuation' procedure, which allows one to decouple the results of the previous method from such variations and, thus, increase confidence in classification results. The respective image processing code developed has been tested successfully on Landsat-5/TM data of the urban area of Athens.     

Modelling and forecasting short-term load demand: A multivariate approach

A multivariable time series model is proposed for short-term load demand forecasting. Unlike other approaches, the order of the model is determined without first finding the coefficients of the model. The Hankel matrix used for determining the order is also utilized for estimating the parameters of the model. This is then compared with order determination using the AIC criterion. Actual data provided by the Ontario Hydro for four loading buses is used for five-minute and hourly forecasts. The results show that the proposed approach is very attractive.     

A collaborative and artificial intelligence approach for semiconductor cost forecasting

Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.     

A hybrid fuzzy and neural approach for DRAM price forecasting

The trend in the price of dynamic random access memory (DRAM) is a very important prosperity index in the semiconductor industry. To further enhance the performance of DRAM price forecasting, a hybrid fuzzy and neural approach is proposed in this study. In the proposed approach, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to forecast the price of a DRAM product. Each fuzzy multiple linear regression model can be converted into two equivalent nonlinear programming problems to be solved. To aggregate these fuzzy price forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy price forecasts into a polygon-shaped fuzzy number, in order to improve the precision. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A real example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of DRAM price forecasting by 66% and 43%, respectively.