Publicacións

@article{Miguélez20091192,

title = {Artificial neural networks in urban runoff forecast},

author = {Mónica Miguélez and Jerónimo Puertas-Agudo and Juan R. Rabuñal-Dopico},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-68749088897&doi=10.1007%2f978-3-642-02478-8_149&partnerID=40&md5=f0f5fea54102aca3715419f81383a183},

doi = {10.1007/978-3-642-02478-8_149},

issn = {03029743},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {5517 LNCS},

number = {PART 1},

pages = {1192-1199},

abstract = {One of the applications of Data Mining is the extraction of knowledge from time series [1][2]. The Artificial Neural Networks (ANNs), one of the techniques of Artificial Intelligence (AI), have proved to be suitable in Data Mining for handling this type of series. This paper presents the use of ANNs and Genetic Algorithms (GA) with a time series in the field of Civil Engineering where the predictive structure does not follow the classic paradigms. In this specific case, the AI technique is applied to a phenomenon that models the process where, for a specific area, the fallen rain concentrates and flows on the surface. © 2009 Springer Berlin Heidelberg.},

note = {cited By 4},

keywords = {AI techniques; Artificial neural network; Artificial neural networks; Predictive structures; Specific areas; Urban runoff, Backpropagation; Civil engineering; Data handling; Genetic algorithms; Mining; Runoff; Sewage; Time series, Neural networks},

pubstate = {published},

tppubtype = {article}

}