| dc.contributor.author |
Glezakos, TJ |
en |
| dc.contributor.author |
Tsiligiridis, TA |
en |
| dc.contributor.author |
Iliadis, LS |
en |
| dc.contributor.author |
Yialouris, CP |
en |
| dc.contributor.author |
Maris, FP |
en |
| dc.contributor.author |
Ferentinos, KP |
en |
| dc.date.accessioned |
2014-06-06T06:47:23Z |
|
| dc.date.available |
2014-06-06T06:47:23Z |
|
| dc.date.issued |
2007 |
en |
| dc.identifier.issn |
16130073 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3562 |
|
| dc.relation.uri |
http://www.scopus.com/inward/record.url?eid=2-s2.0-84884620924&partnerID=40&md5=bfe6c40a04fa16252d18872f5f7b397f |
en |
| dc.subject |
Artificial neural networks |
en |
| dc.subject |
Average annual water supply |
en |
| dc.subject |
Evolutionary time series processing |
en |
| dc.subject |
Genetic algorithms |
en |
| dc.subject |
Genetic ANN training |
en |
| dc.subject |
Maximum volume of water flow |
en |
| dc.subject.other |
ANN trainings |
en |
| dc.subject.other |
Evolutionary approach |
en |
| dc.subject.other |
Evolutionary process |
en |
| dc.subject.other |
Evolutionary training |
en |
| dc.subject.other |
Measuring stations |
en |
| dc.subject.other |
Time series processing |
en |
| dc.subject.other |
Training and testing |
en |
| dc.subject.other |
Water flows |
en |
| dc.subject.other |
Applications |
en |
| dc.subject.other |
Feature extraction |
en |
| dc.subject.other |
Genetic algorithms |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Pattern matching |
en |
| dc.subject.other |
Water supply |
en |
| dc.subject.other |
Watersheds |
en |
| dc.subject.other |
Time series |
en |
| dc.title |
Feature extraction for time series data: An artificial neural network evolutionary training model for the management of mountainous watersheds |
en |
| heal.type |
conferenceItem |
en |
| heal.publicationDate |
2007 |
en |
| heal.abstract |
This manuscript is the result of research conducted towards the production of meta-data to be used as inputs to neural networks. It is essentially a preliminary attempt towards the use of an evolutionary approach to interpret the significance which time series data pose on the behavior of mountainous water supplies, proposing a model which could be effectively used in the estimation of the average annual water supply for the various mountainous watersheds. The data used for the training and testing of the system refer to certain watersheds spread over the island of Cyprus and span a wide temporal period. The method proposed incorporates an evolutionary process to manipulate the time series data of the average monthly rainfall recorded by the measuring stations, while the algorithm includes special encoding, initialization, performance evaluation, genetic operations and pattern matching tools for the evolution of the time series into significantly sampled data. |
en |
| heal.journalName |
CEUR Workshop Proceedings |
en |
| dc.identifier.volume |
284 |
en |