Prediction of Urban Flood Extent by LSTM Model and Logistic Regression

Kim, Hyun Il, Han, Kun Yeun, Lee, Jae Yeong

doi:10.12652/Ksce.2020.40.3.0273

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2020 Vol.40, Issue 3 Preview Page Next Page

Water Engineering

Prediction of Urban Flood Extent by LSTM Model and Logistic Regression LSTM 모형과 로지스틱 회귀를 통한 도시 침수 범위의 예측

June 2020. pp. 273-283

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Abstract

기후변화의 영향으로 국지성 및 집중호우에 대한 발생 가능성이 높아지는 시점에서 과거에 침수피해를 입은 도시 유역에 대하여 실제 호우에 대한 침수 양상을 예측하는 것은 중요하다. 이에 수치해석 기반 프로그램과 함께 기계학습을 이용한 홍수 분석에 대한 연구가 증가하고 있다. 본 연구에서 적용한 LSTM 신경망은 일련의 자료를 분석하는데 유용하지만, 딥 러닝을 수행하기 위하여 충분한 양의 자료를 필요로 한다. 그러나 단일 도시유역에 홍수를 일으킬 강우가 매년 일어나지 않기에 많은 홍수 자료를 수집하기에는 어려움이 있다. 이에 본 연구에서는 대상 유역에서 관측되는 강우 외에 전국 단위의 실제 호우를 예측 모형에 반영하였다. LSTM (Long Short-Term Memory) 신경망은 강우에 대한 총 월류량을 예측하기 위하여 사용되었으며, 목표값으로 SWMM (Storm Water Management Model)의 유출 모의 결과를 사용하였다. 침수 범위 예측을 위해서는 로지스틱 회귀를 사용하였으며, 로지스틱 회귀 모형의 독립 변수는 총 월류량이며 종속 변수는 격자 별 침수 발생 유무이다. 침수 범위 자료는 SWMM의 유출 결과를 바탕으로 수행된 2차원 침수해석 모의 결과를 통해 수집하였다. LSTM의 매개변수 조건에 따라 총 월류량 예측 결과를 비교하였다. 매개변수 설정에 따른 4가지의 LSTM 모형을 사용하였는데, 검증과 테스트 단계에 대한 평균 RMSE (Root Mean Square Error)는 1.4279 $m^{3} / s$ , 1.0079 $m^{3} / s$ 으로 산정되었다. 최소 RMSE는 검증과 테스트에 대하여 각각 1.1656 $m^{3} / s$ , 0.8797 $m^{3} / s$ 으로 산정되었으며, SWMM모의 결과를 적절히 재현할 수 있음을 확인하였다. LSTM 신경망의 결과와 로지스틱 회귀를 연계하여 침수 범위 예측을 수행하였으며, 침수심 0.5m 이상을 고려하였을 때에 최대 침수면적 적합도가 97.33 %으로 나타났다. 본 연구에서 제시된 방법론은 딥 러닝에 기반하여 도시 홍수 대응능력을 향상 시키는데 도움이 될 것으로 판단된다.

키워드

도시 홍수 분석

침수 예측

LSTM 모형

로지스틱 회귀

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning–based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 $m^{3} / s$ , 1.0079 $m^{3} / s$ for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 $m^{3} / s$ and 0.8797 $m^{3} / s$ . It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 $m$ depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

Keywords

Flood prediction

Urban runoff analysis

LSTM model

Logistic regression

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Information

Publisher :Korean Society of Civil Engineers
Publisher(Ko) :대한토목학회
Journal Title :JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS
Journal Title(Ko) :대한토목학회 논문집
Volume : 40
No :3
Pages :273-283
Received Date :2020. 02. 12
Revised Date :2020. 02. 26
Accepted Date : 2020. 04. 06
DOI :https://doi.org/10.12652/Ksce.2020.40.3.0273

JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS ISSN:1015-6348(Print) 2287-934X(Online) 대한토목학회 논문집

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