Development of a Stepwise-Clustered Hydrological Inference Model

Li, Zhong; Huang, Guohe; Han, Jing‐Cheng; Wang, Xiuquan; Fan, Yurui; Cheng, Guanhui; Zhang, Hua; Huang, Wei

doi:10.1061/(asce)he.1943-5584.0001165

Cited by 41 publications

(40 citation statements)

References 55 publications

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“…Moreover, scholars (e.g., [36,56]) argued that incorporating these and other ancillary variables have further increased the accuracy of SAR based soil moisture prediction using the nonlinear regression model, such as SVR and artificial neural network (ANN) techniques. Although using more predictors would lead to more computational complexities, it can help to develop a more comprehensive relationship and further improve the model prediction performance [47].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies (e.g., [45][46][47][48][49]) applied SCA method in different disciplines have shown that SCA model is characterized by higher performance in describing the nonlinear relationship between state variables and dependent variables and better accuracy in predicting observed values. Our findings support these observations using the relationship between volumetric soil moisture and remote sensing data.…”

Section: Discussionmentioning

confidence: 99%

“…For example, [39,43,44] developed a forecasting system using SCA for mapping the link between contaminating concentration and operating conditions in groundwater bioremediation processes. More recently, many works have successfully applied SCA for climate projection [45], stream flow prediction [46], hydrological processes modeling [47,48], and air quality management in an urban environment [49]. All these efforts attested the effectiveness of SCA for environmental and hydrological prediction systems.…”

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confidence: 99%

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Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

et al. 2019

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In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

et al. 2019

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“…In addition, there are also some nonparametric statistical approaches for hydrological predictions. Among them, the stepwise cluster analysis (SCA) method has been widely used for many hydrological applications such as streamflow predictions (Fan et al, 2015a(Fan et al, , 2016aLi et al, 2015Li et al, , 2016, climate downscaling (Wang et al, 2013;Zhuang et al, 2018). The main advantage of SCA is that the inherent relationship between the explanatory and response variables is reflected through cluster trees, which are derived through cutting or merging the sample sets of response variables into new sets based on given criteria.…”

Section: Methods For Hydrological Predictionmentioning

confidence: 99%

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

Fan¹

2019

J ENVIRON INFORM LET

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This study provides a brief review for uncertainty quantification in hydrological predictions. The major approaches for hydrologic predictions are firstly introduced, including the widely used data-driven and process-based modelling approaches. The major uncertainties resulting from inputs, model structures, parameters and outputs are then briefly illustrated. The major review is then conducted for various uncertainty quantification approaches. In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average approaches. Potential issues to be addressed in future are then concluded, summarizing some unclear issues which may be further investigated in further studies.

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“…In addition, a simpler model structure means that the propagation of uncertainty from different sources is easier to assess. The use of data-driven models, such as neural networks, statistical methods or regression-based techniques (e.g., Li et al, 2015b, Li et al, 2015cYang et al, 2015), has been widespread in hydrology, particularly for short term daily flow rate forecasts, using a variety of input variables (Garen, 1992;Zealand et al, 1999;Campolo et al, 1999;Schilling and Walter, 2005;Adamowski and Sun, 2010;Duncan et al, 2011;Li et al, 2015a;Nourani et al, 2015). A recent regression based study predicted flow in the Bow River in Calgary, using a base difference regression model (Veiga et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

Khan¹,

Valeo²

2016

J ENVIRON INFORM

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ABSTRACT.A fuzzy linear regression (FLR) method is proposed that uses real-time data to accurately predict daily peak flow rate for the Bow and Elbow Rivers in southern Alberta. FLR model performance was compared to a non-fuzzy, error-in-variables model (EIV). Mean daily flow rate, with a delay of one, two, three or seven days was used as the independent variable. In implementing the FLR, a unique hybrid modelling approach was devised that treated peak flow rate as probabilistic and mean daily flow rate as possibilistic. Three gauge errors, 5%, 10% and 20%, were tested and compared to quantify uncertainty in observed flow rate. The research proposed a new method of computing the exceedance probability of peak flow rate using fuzzy numbers. NSE, PBIAS and RSR and a proposed rating system were used to evaluate and compare the methods. Two different calibration schemes were used, including a quasi-real time system. The tests demonstrated that FLR with a one day lag was a very good predictor of peak flow rate and outperformed EIV for two stations on the Bow River. A test dataset from the floods of June 2013 in Calgary was used for risk assessment. The FLR results demonstrated higher flexibility and sensitivity to the flood as it approached Calgary. The fuzzy method was able to capture the peak flow rate for the majority of the high flow rate days, while the EIV model was unable to predict this data within the 95% confidence interval.

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Development of a Stepwise-Clustered Hydrological Inference Model

Cited by 41 publications

References 55 publications

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

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