in the paper a model to predict the concentrations of particulate matter pm10, pm2.5, so2, no, co and o3 for a chosen number of hours forward is proposed. the method requires historical data for a large number of points in time, particularly weather forecast data, actual weather data and pollution data. the idea is that by matching forecast data with similar forecast data in the historical data...

This paper investigates the impact of climate change on the productivity of crop production using U.S. county-level yield and weather data between 1950 and 2015. It finds that the pooled estimators used in previous studies underestimate the sensitivity of crops to high temperatures by ignoring slope heterogeneity, and underestimate the damage of future climate change on yield. Furthermore, expl...

This paper presents a particle filtering based long-term RUL prediction method that integrates two data resources: infield Accelerated Degradation testing (ADT) and field operation. This method improves the usage of historical information and makes accurate residual life prediction compared with conventional regression method. ADT data is used as prior information to establish dynamic system mo...

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two...

In most business forecasting applications, the problem usually directs the sampling frequency of the data that we collect and use for forecasting. Conventional approaches try to extract information from the historical observations to build a forecasting model. In this article, we explore how transforming the data through temporal aggregation allows us to gather additional information about the ...

In this paper, a new methodology based on system modelling and optimization is developed to determine forest fire risk, and subsequent resource allocation, over a regional area. A forest fuel moisture model and a propagation model are developed, in order to determine the dynamic risk assessment. The objective of resource allocation is twofold. In the preventive phase means and crews are re-allo...

Natural and man induced nutrient loads affect the functioning of freshwater ecosystems and restrict various water uses. In particular, internal pollution by nutrient remobilisation from sediment plays an important role in shallow water bodies. A sustainable management of such freshwater ecosystems can be achieved by using simulation models. To forecast the eutrophication process of a shallow ri...

This work presents a comparative analysis of Influenzanet data for influenza itself and common cold in the Netherlands during the last 5 years, from the point of view of modelling by linearised SIRS equations parametrically driven by the ambient temperature. It is argued that this approach allows for the forecast of common cold, but not of influenza in a strict sense. The difference in their ki...

Accurate estimates of future age-specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age-specific breast cancer mortality time trends, and forecast entire age-specific mortality functions using a state-space approach...

Forecasts of departure demand are one of the driving inputs to tactical decision-support tools (DSTs) for airport surface traffic. While there are well-known results on averageor worst-case forecast uncertainty, it is the forecast errors which occur under best-case minimum-uncertainty conditions which constrain robust DST design and the achievable traffic benefits. These best-case errors have n...