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SUMMARY:Predicting location of suitable groundwater for brewing coffee usi
 ng tree-based ensemble machine learning
DTSTART;VALUE=DATE-TIME:20191108T071000Z
DTEND;VALUE=DATE-TIME:20191108T072500Z
DTSTAMP;VALUE=DATE-TIME:20260724T113232Z
UID:indico-contribution-23@sshep.snu.ac.kr
DESCRIPTION:Speakers: Hye-Lim Lee (School of Earth and Environmental Scien
 ces\, Seoul National University)\nRecently\, groundwater is used as a sour
 ce of drinking purposes such as water\, coffee\, beer and other beverages.
  The range of water quality for producing high-quality of beverages is dif
 ferent from each usage\, so it is important to find suitable groundwater l
 ocation for each purpose in the aspect of water industry. This study was c
 onducted to predict the suitable location for brewing coffee in Gangwon Pr
 ovince\, South Korea using tree-based ensemble machine learning. Appropria
 te water quality standard for brewing coffee is known as TDS of 75~250 mg/
 L and calcium hardness of 17~85 mg/L from recent research. Boosted Regress
 ion Trees (BRT)\, Random Forests (RF) and Extremely Randomized Trees (ERT)
  were used as tree-based ensemble method. Response indicating suitable or 
 unsuitable groundwater for brewing coffee was determined by 254 wells’ w
 ater quality data\, and predictor variables were composed of slope\, altit
 ude\, drainage grade\, effective soil depth\, soil composition\, land use\
 , and hydrogeology based on GIS data. Applying models to the test data\, a
 ll three models showed the area under a curve (AUC) and accuracy more than
  0.85 and 0.80\, respectively\, which indicates high reliability in predic
 tion. Threshold of dividing suitable or unsuitable for brewing coffee is f
 ound from Receiver Operating Characteristic (ROC) curve. The BRT showed th
 e highest AUC and accuracy among the three models\, therefore\, potential 
 map of suitable groundwater location for brewing coffee was suggested by t
 he BRT model. In the condition of lack of water quality data\, this resear
 ch can help to determine location of suitable groundwater for several usag
 es.\nKeyword: potable groundwater · boosted regression tree · random for
 est · extremely randomized tree \nAcknowledgement: This research was supp
 orted by the National Research Council of Science and Technology(NST) gran
 t funded by the Korea government(MSIP) (No. CAP-17-05-KIGAM)\n\nhttps://ss
 hep.snu.ac.kr/event/107/contributions/23/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/23/
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