Browsing by Author "Casanova-Mateo, Carlos"
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Item On the Application of Multi-objective Harmony Search Heuristics to the Predictive Deployment of Firefighting Aircrafts: a Realistic Case Study: A realistic case study(2015) Bilbao, Miren Nekane; Del Ser, Javier; Salcedo-Sanz, Sancho; Casanova-Mateo, Carlos; IAThis manuscript focuses on the increasing frequency and scales of worldwide wildfires and the need for enhancing the effectiveness of firefighting resources. The scope is focused on optimally deploying firefighting aircrafts on aerodromes and airports existing over an area based on fire risk predictions. This scenario is formulated as a capacity-constrained multi-objective optimisation problem where the utility of the deployed resources with respect to fire forest risk predictions is to be maximised, and expenditures associated with the reallocation of aircrafts must be minimised. This formulation is further complemented by including the impact of the distance from the wildfire to water sources in the firefighting utility function. To efficiently tackle this problem a multi-objective harmony search solver is designed and tested in synthetically generated and real scenarios for the Iberian Peninsula. The results obtained pave the way towards the utilisation of this tool by decision makers when outlining their firefighting logistics.Item The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain: A data-based case study in Madrid, Spain(2016-11-01) Laña, Ibai; Del Ser, Javier; Padró, Ales; Vélez, Manuel; Casanova-Mateo, Carlos; IA; CALIDAD Y CONFORT AMBIENTALUrban air pollution is a matter of growing concern for both public administrations and citizens. Road traffic is one of the main sources of air pollutants, though topography characteristics and meteorological conditions can make pollution levels increase or diminish dramatically. In this context an upsurge of research has been conducted towards functionally linking variables of such domains to measured pollution data, with studies dealing with up to one-hour resolution meteorological data. However, the majority of such reported contributions do not deal with traffic data or, at most, simulate traffic conditions jointly with the consideration of different topographical features. The aim of this study is to further explore this relationship by using high-resolution real traffic data. This paper describes a methodology based on the construction of regression models to predict levels of different pollutants (i.e. CO, NO, NO2, O3 and PM10) based on traffic data and meteorological conditions, from which an estimation of the predictive relevance (importance) of each utilized feature can be estimated by virtue of their particular training procedure. The study was made with one hour resolution meteorological, traffic and pollution historic data in roadside and background locations of the city of Madrid (Spain) captured over 2015. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowed by the effects of stable meteorological conditions of this city.