Flood prediction based on climatic signals using wavelet neural network

Linh, Nguyen Thi Thuy; Ruigar, Hossein; Golian, Saeed; Bawoke, Getnet Taye; Gupta, Vivek; Rahman, Khalil Ur; Sankaran, Adarsh; Pham, Quoc Bao

doi:10.1007/s11600-021-00620-7

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Tytuł artykułu

Flood prediction based on climatic signals using wavelet neural network

Autorzy

Linh Nguyen Thi Thuy , Ruigar Hossein , Golian Saeed , Bawoke Getnet Taye , Gupta Vivek , Rahman Khalil Ur , Sankaran Adarsh , Pham Quoc Bao

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00620-7

Warianty tytułu

Języki publikacji

Abstrakty

Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research. The large-scale circulation is often quantifed in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefcient of correlation (R), and Nash–Sutclife coefcient (CNS) were used to quantify the performance of prediction of maximum monthly discharge of three diferent hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learning model was found to be giving superior performance consistently against the standalone artifcial neural network (ANN) model and multiple linear regression model to predict the food discharges of March and August months. The prediction of food for August which is more devastating is found to be slightly better than the prediction of foods of March, in the stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to be 0.68, 0.996, and 0.99 m3 /s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and 2013, the wavelet-based neural networks performed remarkably well than the ANN.

Słowa kluczowe

large-scale climatic circulation sea surface temperature sea level pressure artificial neural network maximum discharge prediction

cyrkulacja klimatyczna na dużą skalę temperatura powierzchni morza ciśnienie na poziomie morza sztuczna sieć neuronowa

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2021

Tom

Vol. 69, no. 4

Strony

1413--1426

Opis fizyczny

Bibliogr. 75 poz.

Twórcy

autor

Linh Nguyen Thi Thuy

Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

autor

Ruigar Hossein

Civil Engineering Department, Shahrood University of Technology, Shahrud, Iran

autor

Golian Saeed

Civil Engineering Department, Shahrood University of Technology, Shahrud, Iran
ICARUS, Department of Geography, Maynooth University, Maynooth, Ireland

autor

Bawoke Getnet Taye

Department of Geology, School of Earth Sciences, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia

autor

Gupta Vivek

Department of Hydrology, Indian Institute of Technology, Roorkee, India

autor

Rahman Khalil Ur

State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

autor

Sankaran Adarsh

Department of Civil Engineering, TKM College of Engineering, Kollam 691005, India

autor

Pham Quoc Bao

phambaoquoc@tdmu.edu.vn

Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City, Binh Duong Province, Vietnam

https://orcid.org/0000-0002-0468-5962

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Bibliografia

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