Data mining methods to generate severe wind gust models
| aut.relation.endpage | 80 | |
| aut.relation.issue | 1 | |
| aut.relation.pages | 80 | |
| aut.relation.startpage | 60 | |
| aut.relation.volume | 5 | |
| aut.researcher | Shanmuganathan, Subana | |
| dc.contributor.author | Shanmuganathan, S | |
| dc.contributor.author | Sallis, P | |
| dc.date.accessioned | 2014-01-29T00:54:43Z | |
| dc.date.available | 2014-01-29T00:54:43Z | |
| dc.date.copyright | 2014-01-13 | |
| dc.date.issued | 2014-01-13 | |
| dc.description.abstract | Gaining knowledge on weather patterns, trends and the influence of their extremes on various crop production yields and quality continues to be a quest by scientists, agriculturists, and managers. Precise and timely information aids decision-making, which is widely accepted as intrinsically necessary for increased production and improved quality. Studies in this research domain, especially those related to data mining and interpretation are being carried out by the authors and their colleagues. Some of this work that relates to data definition, description, analysis, and modelling is described in this paper. This includes studies that have evaluated extreme dry/wet weather events against reported yield at different scales in general. They indicate the effects of weather extremes such as prolonged high temperatures, heavy rainfall, and severe wind gusts. Occurrences of these events are among the main weather extremes that impact on many crops worldwide. Wind gusts are difficult to anticipate due to their rapid manifestation and yet can have catastrophic effects on crops and buildings. This paper examines the use of data mining methods to reveal patterns in the weather conditions, such as time of the day, month of the year, wind direction, speed, and severity using a data set from a single location. Case study data is used to provide examples of how the methods used can elicit meaningful information and depict it in a fashion usable for management decision making. Historical weather data acquired between 2008 and 2012 has been used for this study from telemetry devices installed in a vineyard in the north of New Zealand. The results show that using data mining techniques and the local weather conditions, such as relative pressure, temperature, wind direction and speed recorded at irregular intervals, can produce new knowledge relating to wind gust patterns for vineyard management decision making. | |
| dc.identifier.citation | Atmosphere 2014, vol.5(1), pp.60 - 80 (80) | |
| dc.identifier.doi | 10.3390/atmos5010060 | |
| dc.identifier.issn | 2073-4433 | |
| dc.identifier.uri | https://hdl.handle.net/10292/6622 | |
| dc.language | English | |
| dc.publisher | MDPI AG, Basel, Switzerland. | |
| dc.relation.isreplacedby | 10292/7699 | |
| dc.relation.isreplacedby | http://hdl.handle.net/10292/7699 | |
| dc.relation.uri | http://dx.doi.org/10.3390/atmos5010060 | |
| dc.rights | MDPI is a RoMEO green publisher — RoMEO is a database of Publishers' copyright and self-archiving policies hosted by the University of Nottingham | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Weather extremes | |
| dc.subject | Wind speed and direction | |
| dc.subject | Artificial neural networks | |
| dc.subject | Self-organising map (SOM) method | |
| dc.title | Data mining methods to generate severe wind gust models | |
| dc.type | Journal Article | |
| pubs.elements-id | 160469 | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Vice Chancellor's Group |