Searching a site for a civil airport based on bird ecological conservation: An expert-based selection (Dalian, China)

A Route Planning Method for UAV Swarm Inspection of Roads Fusing Distributed Droneport Site Selection

Current methods that use Unmanned Aerial Vehicle (UAV) swarms to inspect roads still have many limitations in practical applications, such as the lack of or difficulty in the route planning, the unbalanced utilization rate of the UAV swarm and the difficulty of the site selection for the distributed droneports. To solve the limitations, firstly, we construct the inspection map and remove the redundant information irrelevant to the road inspection. Secondly, we formulate both the route planning problem and the droneport site selection problem in a unified multi-objective optimization model. Thirdly, we redesign the encoding strategy, the updating rules and the decoding strategy of the particle swarm optimization method to effectively solve both the route planning problem and the droneport site selection problem. Finally, we introduce the comprehensive evaluation indicators to verify the effectiveness of the route planning and the droneport site selection. The experimental results show that (1) with the proposed method, the overlapped part of the optimized inspection routes is less than 7% of the total mileage, and the balanced utilization rate of the UAVs is above 75%; (2) the reuse rate of the distributed droneports is significantly improved after optimization; and (3) the proposed method outperforms the ant colony optimization (ACO) method in all evaluation indicators. To this end, the proposed method can effectively plan the inspection routes, balance the utilization of the UAVs and select the sites for the distributed droneports, which has great significance for a fully autonomous UAV swarm inspection system for road inspection.

Using DNA barcoding and field surveys to guide wildlife management at Nanjing Lukou International Airport, China

The conflicts between wildlife and aircraft have increased due to the development of the aviation industry. While many studies have quantified the relative hazards of wildlife to aircraft, few studies have combined DNA barcoding techniques with field surveys of bird communities in different habitats to reveal the exact species involved in bird strikes and how the habitat heterogeneity around airports affects bird communities and even the occurrence of bird strikes. Taking Nanjing Lukou International Airport in China as an example, based on the DNA barcoding technology and detailed field research, we establish the most commonly struck species, which can help managers identify the level of hazard and lead to meaningful reductions in hazards and costs associated with bird strike. The investigation of bird communities showed that there were 149 bird species recorded within an 8 km radius. There were 89, 88, 61, and 88 species in the woodland, wetland, farmland, and urban area, respectively. In total, 303 samples identified 82 species representing 13 orders and 32 family of birds from bird strike cases, of which 24 species were not found in the field survey. Passeriformes were the most common order of birds identified, with 43 species represented in 167 identifications. Skylark, Thrush, Shrike, Lapwing, and Swallow were most likely to cause damage or substantial damage to aircraft when strikes occurred. In addition to birds, we identified 69 bats individuals (accounting for 22.77%) using DNA barcoding. The Bray-Curtis similarity analysis revealed that species involved in bird strike had the highest similarity with urban area. Our findings suggest that policymakers should pay more attention to managing the wetlands and urban areas surrounding the airport. These findings imply that DNA barcoding can add to the environmental monitoring in airports, which can facilitate hazard management and improve air safety.

Three novel bird strike likelihood modelling techniques: The case of Brisbane Airport, Australia

The risk posed by wildlife to air transportation is of great concern worldwide. In Australia alone, 17,336 bird-strike incidents and 401 animal-strike incidents were reported to the Air Transport Safety Board (ATSB) in the period 2010-2019. Moreover, when collisions do occur, the impact can be catastrophic (loss of life, loss of aircraft) and involve significant cost to the affected airline and airport operator (estimated at globally US$1.2 billion per year). On the other side of the coin, civil aviation, and airport operations have significantly affected bird populations. There has been an increasing number of bird strikes, generally fatal to individual birds involved, reported worldwide (annual average of 12,219 reported strikes between 2008-2015 being nearly double the annual average of 6,702 strikes reported 2001-2007) (ICAO, 2018). Airport operations including construction of airport infrastructure, frequent take-offs and landings, airport noise and lights, and wildlife hazard management practices aimed at reducing risk of birdstrike, e.g., spraying to remove weeds and invertebrates, drainage, and even direct killing of individual hazard species, may result in habitat fragmentation, population decline, and rare bird extinction adjacent to airports (Kelly T, 2006; Zhao B, 2019; Steele WK, 2021). Nevertheless, there remains an imperative to continually improve wildlife hazard management methods and strategies so as to reduce the risk to aircraft and to bird populations. Current approved wildlife risk assessment techniques in Australia are limited to ranking of identified hazard species, i.e., are 'static' and, as such, do not provide a day-to-day risk/collision likelihood. The purpose of this study is to move towards a dynamic, evidence-based risk assessment model of wildlife hazards at airports. Ideally, such a model should be sufficiently sensitive and responsive to changing environmental conditions to be able to inform both short and longer term risk mitigation decisions. Challenges include the identification and quantification of contributory risk factors, and the selection and configuration of modelling technique(s) that meet the aforementioned requirements. In this article we focus on likelihood of bird strike and introduce three distinct, but complementary, assessment techniques, i.e., Algebraic, Bayesian, and Clustering (ABC) for measuring the likelihood of bird strike in the face of constantly changing environmental conditions. The ABC techniques are evaluated using environment and wildlife observations routinely collected by the Brisbane Airport Corporation (BAC) wildlife hazard management team. Results indicate that each of the techniques meet the requirements of providing dynamic, realistic collision risks in the face of changing environmental conditions.

Combining AHP and ROC with GIS for Airport Site Selection: A Case Study in Libya

Choosing airport locations requires thorough and comprehensive decisions to be made. To do so in a professional and logical manner is crucial for the social, economic, and logistic settings intended for any region. The present research takes place in Libya, where airports are just as vital for the economy in terms of tourism and investment by allowing for improved transportation throughout the developing market and supplier locations as well as trading between the industrial and financial sectors. For this reason, using the geographic information system (GIS) to determine the appropriate airport site, twenty-three criteria were considered. In addition, two different methods—analytic hierarchy process (AHP) and rank order centroid (ROC)—were utilized to derive the related weights. The comparison of the output maps from these two distinctive approaches shows that both approaches provide identical results. Finally, a sensitivity analysis was carried out to evaluate the reliability of the method used and select the best site among the proposed ones based on the result of the highest suitability index for each candidate site. This research provides a siting approach and substantial support for decision-makers in the issue of airport locations selection in Libya and other developing countries.