Special Issue "Integration of Advanced Soft Computing Techniques in Hydrological Predictions"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land - Atmosphere Interactions".

Deadline for manuscript submissions: closed (15 October 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Kwok-wing Chau

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong
Website | E-Mail
Interests: artificial intelligence; hydrology; soft computing; water quality; meta-heuristic algorithm; hydrodynamic; rainfall; runoff

Special Issue Information

Dear Colleagues,

Extreme weather events, occurring more frequently in recent years possibly due to climate change, result in enormous economic and human losses globally every year. It is important to have the capability to predict accurately both the occurrence time and magnitude of peak flow in advance of an impending extreme weather event. The integration of soft computing techniques in hydrological predictions is a growing field of endeavor in water resources engineering and management. It can be employed to optimally calibrate data-driven hydrological models so as to enhance the forecasting accuracy. This special edition of the Atmosphere journal is tailored to fill the existing gap by including papers on the advancement in the contemporary use of soft computing techniques in hydrological modelling. The information and analyses are intended to contribute to the development and implementation of effective hydrological prediction and thus appropriate precautionary measures.

Prof. Kwok Wing Chau
Guest Editor

Manuscript Submission Information

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Keywords

  • hydrological
  • prediction
  • modelling
  • soft computing
  • meta-heuristic
  • data-driven

Published Papers (2 papers)

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Research

Open AccessArticle Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff
Atmosphere 2018, 9(7), 251; https://doi.org/10.3390/atmos9070251
Received: 24 May 2018 / Revised: 2 July 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
Cited by 1 | PDF Full-text (4336 KB) | HTML Full-text | XML Full-text
Abstract
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of
[...] Read more.
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling. Full article
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Open AccessArticle Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method
Atmosphere 2018, 9(2), 71; https://doi.org/10.3390/atmos9020071
Received: 18 January 2018 / Revised: 9 February 2018 / Accepted: 10 February 2018 / Published: 15 February 2018
Cited by 2 | PDF Full-text (4412 KB) | HTML Full-text | XML Full-text
Abstract
Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to
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Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins. Full article
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