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논문 기본정보

Classification of Water Level Fluctuation Data in Wells using Linear Regression Models and Genetic Algorithm

논문 개요

기관명, 저널명, ISSN, ISBN 으로 구성된 논문 개요 표입니다.
기관명 NDSL
저널명 應用地質 = Journal of the Japan Society of Engineering Geology
ISSN 0286-7737,
ISBN

논문저자 및 소속기관 정보

저자, 소속기관, 출판인, 간행물 번호, 발행연도, 초록, 원문UR, 첨부파일 순으로 구성된 논문저자 및 소속기관 정보표입니다
저자(한글) WAKAMATSU, Hisanori,WATANABE, Kunio,TAKEUCHI, Shinji,SAEGUSA, Hiromitsu
저자(영문)
소속기관
소속기관(영문)
출판인
간행물 번호
발행연도 2008-01-01
초록 It is important to understand and quantitatively classify the characteristics of local groundwater flow indicated by the water level fluctuation in wells. In this study a method to evaluate the similarities of water level fluctuation between wells is proposed. Linear regression models are constructed with independent variables such as rainfall and water level of other wells. Well similarity is estimated from model parameters (regression coefficients and model fitness). Regression coefficients are calculated with Genetic Algorithm (GA); with GA identification of parameters is easier even in a complicated model. The method was applied to twelve wells in the Tono area in central Japan. Although groundwater level fluctuation is primarily affected by rainfall and pumping conditions, different geological conditions should also cause different types of water level. Models using water level in other wells, as well as models using preceding rainfalls and atmospheric pressure, suggest that water level fluctuation data of the wells are classified into groups reflecting the geological conditions. This is explained by the difference in the property of pressure propagation for rain infiltration among the units. Additionally, comparison of model fitness between the models can be used for estimating the extent of these factors' effect
원문URL http://click.ndsl.kr/servlet/OpenAPIDetailView?keyValue=03553784&target=NART&cn=NART51543563
첨부파일

추가정보

과학기술표준분류, ICT 기술분류,DDC 분류,주제어 (키워드) 순으로 구성된 추가정보표입니다
과학기술표준분류
ICT 기술분류
DDC 분류
주제어 (키워드) groundwater level fluctuation,classification,linear regression model,genetic algorithm