메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Suin Park (Pusan National University) Minh Thi Nguyen (Vietnam Academy of Science and Technology) Junbeom Jeon (Pusan National University) Hyokwan Bae (Ulsan National Institute of Science and Technology (UNIST))
저널정보
대한환경공학회 Environmental Engineering Research Environmental Engineering Research 제29권 제4호
발행연도
2024.8
수록면
126 - 140 (15page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The bacterial community structure of polluted soil differentiates according to toxic pollutants. In this study, the acid pollution source was predicted by using characteristic bacterial community structures which were exposed to HCl, HF, HNO₃, and H₂SO₄. In a soil column, after a simulated acid leak, Bacillus, Citrobacter, Rhodococcus, and Ralstonia sp. were found as acid-resistant bacteria and their relative abundance varied depending on the acid. The complex bacterial community was analyzed by using terminal restriction fragment length polymorphism (T-RFLP) of 16S rRNA gene. Using machine learning models including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and artificial neural network (ANN), the prediction accuracy for acidic pollutants was 72%, 72%, 76%, and 88%, respectively. With data augmentation based on T-RFLP, the accuracy of the ANN model for predicting acidic pollutants improved to 98%. This research provides valuable insights into the potential use of bacterial community structures and machine learning models for the rapid and accurate identification of acid pollution sources in soil.

목차

ABSTRACT
1. Introduction
2. Materials and Methods
3. Results & Discussion
4. Conclusions
References

참고문헌 (91)

참고문헌 신청

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-151-24-02-089362965