EurekaMag.com logo
+ Site Statistics
References:
53,214,146
Abstracts:
29,074,682
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

Neural net for determining DEM-based model drainage pattern


Journal of Irrigation and Drainage Engineering 122(2): 112-121
Neural net for determining DEM-based model drainage pattern
Manually determining drainage patterns from topographical maps for a grid-based model is time consuming and occasionally subjective. Eight methods including neural network were developed to automatically determine the pattern from Digital Elevation Model (DEM) data. These were tested for a subwatershed in Taiwan.

(PDF same-day service: $19.90)

Accession: 002903602

DOI: 10.1061/(asce)0733-9437(1996)122:2(112)



Related references

A neural network model for schemas based on pattern completion. Journal of the American Academy of Psychoanalysis and Dynamic Psychiatry 39(2): 243-261, 2011

A neural network model for transference and repetition compulsion based on pattern completion. Journal of the American Academy of Psychoanalysis and Dynamic Psychiatry 36(2): 255-278, 2008

A spike-timing pattern based neural network model for the study of memory dynamics. Plos One 4(7): E6247-E6247, 2009

An individual-specific gait pattern prediction model based on generalized regression neural networks. Gait & Posture 39(1): 443-448, 2014

Model-based policy support in nature conservation: determining the optimal land use pattern in the agrarian landscape of the MAB biosphere reserve Mittlere Elbe. Agrarwirtschaft 53(3): 131-141, 2004

Determining drainage pattern using DEM data for nonpoint-source water quality modeling. Water Science & Technology 26(1-11): 1431-1438, 1992

Wavelet-based neural pattern analyzer for behaviorally significant burst pattern recognition. Conference Proceedings 2008: 38-41, 2009

Determining the characteristics of water pollutants by neural sensors and pattern recognition methods. Journal Of Chromatography A. 722(1-2): 233-243, 1996

Model trials for determining the best technological variant of infiltration slot drainage. Archiv fur Acker und Pflanzenbau und Bodenkunde: 0 (8) 581-588, 1976

Determining surface drainage by extracting thalweg networks from a digital terrain model. Proceedings of the International Cartographic Conference 17, Vol, 1995