Coupling hydrological modeling and support vector regression to model hydropeaking in alpine catchments

Chiogna, Gabriele; Marcolini, Giorgia; Liu, Wanying; Ciria, Teresa Pérez; Tuo, Ye

doi:10.1016/j.scitotenv.2018.03.162

Cited by 29 publications

(20 citation statements)

References 61 publications

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“…SVM is a black box, mathematic model, which attempts to search for an optimal separating hyperplane with the maximal margin between observations and finds the optimal function and parameter sets fitting the observations while avoiding overfitting and having better generalization ability [19]. SVR belongs to an application of SVM for regression analysis.…”

Section: Plos Onementioning

confidence: 99%

“…In the k-fold cross-validation, the dataset was subdivided into k subsets of nearly equal size. In each step, the k-1 subsets were used to train the model while the remaining subset was used for validation [19]. Each subset was applied exactly once for validation.…”

Section: Plos Onementioning

confidence: 99%

“…SVM is a data-driven machine learning model that has been widely applied to hydrologic prediction, such as short-term or long-term streamflow and sediment yield forecasting [4,[15][16][17][18][19], water quality prediction [20,21], precipitation, temperature and evapotranspiration simulation [22,23], and the process of parameterization [12]. The essential characteristic of the SVM method is its ability to efficiently and accurately predict the nonlinear relationship between input and output variables without considering their internal physical links.…”

Section: Introductionmentioning

confidence: 99%

“…Several works have evaluated the performance of SWAT and SVM in streamflow prediction [12,19,28]. Zhang, Srinivasan [12] et al applied Artificial Neutral Network (ANN) and SVM methods to identify the optimal SWAT parameters to save the time cost of calibration and improve the efficiency of parameter calibration in two watersheds of the U.S. Jajarmizadeh, Kakaei Lafdani [28] et al compared the monthly streamflow predictions from SWAT and SVM, and found the SVM model had a closer value for the average flow in comparison to the SWAT model.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have combined the two methods for a hybrid approach to streamflow prediction. Chiogna, Marcolini [19] et al developed an SVM with SWAT model to predict hydropeaking in alpine watersheds in the Northeastern Italian. They used SVM to train the output of SWAT and found the SVM model can capture the fluctuation in streamflow.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Yuan

Forshay

2021

PLoS ONE

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Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Yuan

Forshay

2021

PLoS ONE

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Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data

Yang

Zhang

Xia

et al. 2022

Intl Journal of Climatology

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The timely and effective early warning of sudden drought events is of great importance for reducing disaster losses and drought risk. Satellite microwave remote sensing with the advanced microwave scanning radiometer (AMSR) has enabled the global monitoring of multiple components of water, such as soil moisture (SM), vapour pressure deficit (VPD), and open water fraction (OWF), on a daily scale since 2002. In this study, the applicability of the AMSR surface wetness index (ASWI) and drought features in Central Asia were studied from 2002 to 2017 based on AMSR data. This study concluded that (1) the spatial and temporal differences in the various water components and coefficient of variation of the Central Asia land-atmosphere interface were large, which led to large spatial and temporal differences under both dry and wet conditions in Central Asia; (2) although the ASWI was not significantly correlated with other drought indices (i.e., gravity recovery and climate experiment (GRACE) combined climatological deviation index (CCDI) and GRACE drought severity index (DSI)) over Central Asia, the maximum Pearson correlation coefficient (CC) between the ASWI and Palmer drought severity index (PDSI) in the Irtysh River Basin was 0.824 (p < 0.05), corresponding to a moving average window of 11 months. The CCs of ASWI and Standardized Precipitation Evapotranspiration Index on a 6-month scale (SPEI-6) in Central Asia were generally more than 0.8 (p < 0.05), indicating that the ASWI was effective for retrieving drought conditions in Central Asia. In addition, these four drought indices (ASWI, PDSI, GRACE-DSI, and GRACE-CCDI) revealed that drought occurred continuously after 2003 in Central Asia; (3) the spatial and temporal distribution of dry and wet conditions in Central Asia is closely related to the teleconnection indices (i.e., MEI, SOI, and PDO). Thus, AMSR data are critical for understanding drought events and their evolution in regions influenced by climate change.

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Sensitivity analysis in the wavelet domain: a comparison study

Chiogna,

Marcolini,

Engel

et al. 2024

Stoch Environ Res Risk Assess

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Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

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Coupling hydrological modeling and support vector regression to model hydropeaking in alpine catchments

Cited by 29 publications

References 61 publications

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data

Sensitivity analysis in the wavelet domain: a comparison study

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