Rawat, S. S. <br/><br/>
MODELLING OF SPRING FLOW USING ARTIFICIAL NEURAL NETWORK 

 - Vol,39(3), July 
 - Roorkee  Indian Water Resources Society  2019 
 - 10-17p 
<br/><br/> n the mountainous region, springs are the main sources of drinking water supply as well as domestic water, therefore these s<br/>of the region. During summer, spring flow reduces significantly and sometimes they dried<br/>hydrology is complex in nature and lack of a database on springs restricts the application of developed hydrological models <br/>critical waters resources as a sustainable source of water in the re<br/>developed to predict the spring flow for two springs namely, Hill Campus and Fakua from Tehri Garhwal district of Uttarakhand<br/>data of precipitation (P), temperature (T), relative humidity (RH) and spring flow (Q) from the year 1999 to the year 2003 were used to <br/>model the spring flow. First, four-year data was used to train the models, whereas, remaining one<br/>models in predicting the springflow. Seven models differ in input parameters (P, T, RH & Q) have been developed and the best fit model was <br/>selected on the basis of capability of the network to converge normalize system error (NSE). Developed models performance wer<br/>by categorize all data samples into six ranges (0<br/>with observed data. A close agreement between observed and ANN predicted spring flow in the testing period for both the sprin<br/>the capability of ANN in predicting the spring flow even in data scare situation. The study also reveals that variability in <br/>results in better accuracy in the testing period 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Civil Engineering