Information Technology and Computer Application Engineering – Liu, Sung & Yao (Eds)
© 2014 Taylor & Francis Group, London, ISBN 978-1-138-00079-7
Water quality remote retrieve model based on Neural Network
for dispersed water source in typical hilly area
X.J. Long & Y. Ye
College of Resources and Environment, Southwest University, Chongqing, China
C.M. Zhang
College of Water Resource and Hydropower & State Key Library of Hydraulics
and Mountain River Engineering, Sichuan University, Chengdu, China
ABSTRACT: Based on TM image data and synchronization of the measured data, this manuscript constructs
the quantitative inversion model of water quantity parameters, in view of distributed water resource inYongchuan
distinct. In order to compare the inversion ...