[논문]지하수위 예측을 위한 경사하강법과 화음탐색법의 결합을 이용한 다층퍼셉트론 성능향상

이원진; 이의훈

doi:10.3741/jkwra.2022.55.11.903

지하수위 예측을 위한 경사하강법과 화음탐색법의 결합을 이용한 다층퍼셉트론 성능향상
Improvement of multi layer perceptron performance using combination of gradient descent and harmony search for prediction of ground water level 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.55 no.11, 2022년, pp.903 - 911

이원진 (충북대학교 토목공학과) , 이의훈 (충북대학교 토목공학부)

초록
AI-Helper

물을 공급하기 위한 자원 중 하나인 지하수는 다양한 자연적 요인에 의해 수위의 변동이 발생한다. 최근, 인공신경망을 이용하여 지하수위의 변동을 예측하는 연구가 진행되었다. 기존에는 인공신경망 연산자 중 학습에 영향을 미치는 Optimizer로 경사하강법(Gradient Descent, GD) 기반 Optimizer를 사용하였다. GD 기반 Optimizer는 초기 상관관계 의존성과 해의 비교 및 저장 구조 부재의 단점이 존재한다. 본 연구는 GD 기반 Optimizer의 단점을 개선하기 위해 GD와 화음탐색법(Harmony Search, HS)를 결합한 새로운 Optimizer인 Gradient Descent combined with Harmony Search(GDHS)를 개발하였다. GDHS의 성능을 평가하기 위해 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용하여 이천율현 관측소의 지하수위를 학습 및 예측하였다. GD 및 GDHS를 사용한 MLP의 성능을 비교하기 위해 Mean Squared Error(MSE) 및 Mean Absolute Error(MAE)를 사용하였다. 학습결과를 비교하면, GDHS는 GD보다 MSE의 최대값, 최소값, 평균값 및 표준편차가 작았다. 예측결과를 비교하면, GDHS는 GD보다 모든 평가지표에서 오차가 작은 것으로 평가되었다.

Abstract ▼ AI-Helper

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

주제어

표/그림 (13)

그림 Fig. 1. Structure of MLP
그림 Fig. 2. Schematic figure of GD
그림 Fig. 3. Flowchart of HS
그림 Fig. 4. Flowchart of GDHS
그림 Fig. 5. Learning data of MLP
그림 Fig. 6. Location of study sites
표 Table 1. Result of TLCC
그림 Fig. 7. Conversion shape of GD and GDHS
그림 Fig. 8. Comparison of the training results
표 Table 2. Training results of MLP using GD and GDHS
표 Table 3. MSE of prediction results using GD and GDHS
표 Table 4. MAE of prediction results using GD and GDHS
그림 Fig. 9. Comparison of the prediction results

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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