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Using monitoring history data to build and to train a prediction model for airport noise is a normal method in recent years. However, the single model built in different ways has various performances in the storage, efficiency and accuracy. In order to predict the noise accurately in some complex environment around airport, this paper presents a prediction method based on hybrid ensemble learning. The proposed method ensembles three algorithms: artificial neural network as an active learner, nearest neighbor as a passive leaner and nonlinear regression as a synthesized learner. The experimental results show that the three learners can meet forecast demands respectively in on- line, near-line and off-line. And the accuracy of prediction is improved by integrating these three learners’ results.

Airport noise is a long-standing problem with significant interests, because it is strongly related to both economic growth and the wellbeing of citizens. On many occasions, knowledge about reducing airport noise is shared. This may or may not result in policy change. This paper investigates how the interplay between the domestic regulatory structure and style on the one hand and the type of knowledge use on the other, determines policy change on the basis of internationally gained knowledge. It does so by analysing policies to reduce annoyance by day-time noise around Heathrow, Amsterdam Airport Schiphol and Charles de Gaulle. The three cases show the importance of a balance between regulatory structure and style and the type of knowledge use for policy change based on international gained knowledge.

Airports owned by the state, as it is the case for most Romanian Airports, have little decision-making competence in Land-use Planning. In this case, Communication and Engagement with all relevant stakeholders, as well as with residents from communities surrounding the airport, can become crucial for the prevention of encroachment. In such cases, in the absence of a complete and harmonised legislative framework for airport noise, it is difficult to understand and choose an effective intervention to reduce noise exposure and impact.

Guangzhou Baiyun International Airport (BIA) is the core airport of Guangdong–Hong Kong–Macau Greater Bay Area. This study produced the noise maps of BIA during summer and winter using a simplified calculation method for the weighted equivalent continuous perceived noise level (LWECPN). Particularly, this method used open-source flight data and short-term noise measurement to replace the traditional long-term noise measurement method. The accuracy of the developed noise map was verified by the field experimental data with an average error of 1.5 dB. The noise maps were analyzed in many aspects including the area and population under different noise levels, the spatial distribution of the aircraft noise, the distribution of noise sensitive points, and the land use condition around BIA. It was manifested that about 22.22% and 25.46% of the total population of the five administrative regions were exposed to LWECPN > 70 dB during summer and winter, respectively. The total area within the noise-affected area that violated the noise limit were about 18.740 km2 and 18.109 km2 during summer and winter, respectively.

Airport noise is noise pollution produced by any aircraft or its components, during various phases of a flight,especially the landing phases. This paper study the airport noise prediction model basing on WECPNL(Weighted Equivalent Continuous Perceive Noise Level) recommended by ICAO as a index. With the measurement data,which is properly distribute to the final of active runway and centerline, a brief summary of WECPNL-based prediction model from touchdown to runway centerline is established. However, the results showed a discrepancy of certain points was between prediction and measurement data, especially 1.5-2.5km away from the airport. Therefore, an amendment was proposed with greater accuracy, which the relative of the new model for future prediction ranged from 1.0% to 2.0%. To some extent, this model also can reflect and make full use of the limited data with a high accuracy, especially suit for monitoring rapidly in some new small airports.

Systematically analyzes the basic principle, calculation methods and control standards of the noise metrics of the world's major countries and regions. A series of experiments were taken on based on the noise monitoring data of a large busy airport. Through the experimental analysis demonstrated the similarities and differences of the airport noise metrics, pointing out that the existing airport noise metrics are not complete and need improvement.

This paper examines interactions between stakeholders in negotiating the acceptability of aircraft noise standards in Australia and some lessons arising. It investigates the interplay of the politics of noise within a broader spatial context of development and planning coordination focusing on the interplay of government, airports, community interests, and the development sector. Different local environments inevitably frame diverse contexts, but the pervasive challenge is securing the acceptable trade-off between the economic dividends promised by airports and local quality of life. Discussion is structured around four main key issues: an introduction to the Australian politics of airport noise, an historical timeline of key contextual events, identification of the major actors in the noise governance framework, and a focus on an issue of increasing political significance, namely the different positions of airports and developers in the increasing intensification of urban development.

Presentation at the AEC 2020 conference

Regional airports are often located very close to the urban area they serve and the increasing traffic rate that many of them have experienced in the last years has produced several impacts on the communities living close to the airport area, mainly aviation noise. If not properly managed, noise impacts produced by airport operations can cut down significantly the development of airport air traffic with direct effects on the economic and territorial systems. Aeronautical noise has greatly reduced in the last decade, due to aircraft design technological improvements and more severe regulations. However, the noise reduction during a single event does not make the issue of the airport location – and then the whole noise impact – less significant. This paper proposes an assessment process to evaluate the effects of actions adopted to reduce airport noise impacts on populated areas. Both airport-related factors – such as number of take-off; day-evening-night distributions of movements; aircraft type; flying paths – and land-use characteristics have been considered and combined in a density index that synthesizes the impacts of airport noise on the territory. The assessment process has been tested on a real case, the airport of Bologna in Northern Italy. The predicted results, compared with available real data for the test case, are significant and encourage the use of the proposed assessment process as decision support system for the airport management.