Click for new scientific resources and news about Corona[COVID-19]

Paper Information

Journal:   JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)   JANUARY-FEBRUARY 2017 , Volume 30 , Number 6 ; Page(s) 1874 To 1887.
 
Paper: 

DAILY DISCHARGE ESTIMATION IN TALAR RIVER USING LAZY LEARNING MODEL

 
 
Author(s):  ABDOLLAHI Z., KAVIAN A.*, SHAHEDI K., ABDOLLAHI N., JAFARI M.
 
* COLLEGE OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES, SARI UNIVERSITY
 
Abstract: 

Introduction: River discharge as one of the most important hydrology factors has avital role in physical, ecological, social and economic processes. So, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface water management, design of hydraulic structures, flood control and ecological studies in spetialand temporal scale. Therefore, in last decades different techniques for short-term and long-term estimation of hourly, daily, monthly and annual discharge have been developed for many years. However, short-term estimation models are less sophisticated and more accurate. Various global and local algorithms have been widely used to estimate hydrologic variables. The current study effort to use Lazy Learning approach to evaluate the adequacy of input data in order to follow the variation of discharge and also simulate next-day discharge in Talar River in Kasilian Basinwhere is located in north of Iran with an area of 66.75 km2. Lazy learning is a local linear modelling approach in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.

 
Keyword(s): DAILY DISCHARGE, KASILIAN BASIN, LOCAL LAZY LEARNING MODEL, NEAREST NEIGHBORHOOD
 
References: 
  • ندارد
 
  pdf-File tarjomyar Persian Abstract Yearly Visit 58
 
Latest on Blog
Enter SID Blog