Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

Elshorbagy, Amin; Corzo, Gerald; Srinivasulu, S.; Solomatine, Dimitri

doi:10.5194/hess-14-1931-2010

Cited by 174 publications

(85 citation statements)

References 46 publications

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“…Data-driven models, such as neural networks, regression based techniques, fuzzy rule based systems, and genetic programming, have seen widespread use in hydrology, including DO prediction in rivers (Shrestha and Solomatine, 2008;Solomatine et al, 2008;Elshorbagy et al, 2010). Wen et al (2013) used artificial neural networks (ANNs) to predict DO in a river in China using ion concentration as the predictors.…”

Section: Fuzzy Numbers and Data-driven Modellingmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are a type of data-driven model that are defined as a massively parallel distributed information processing system (Elshorbagy et al, 2010;Wen et al, 2013). ANN models have been widely used in hydrology when the complexity of the physical systems is high owing partially to an incomplete understanding of the underlying process and the lack of availability of necessary data (He et al, 2011;Kasiviswanathan et al, 2013).…”

Section: Fuzzy Neural Network 231 Background On Artificial Neural mentioning

confidence: 99%

“…A trained MLP network can be used as a universal approximator with only one hidden layer (Hornik et al, 1989). This means that models are relatively simple to develop and theoretically have the capacity to approximate any linear or non-linear mapping (ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000; Elshorbagy et al, 2010;Napolitano et al, 2011;Kasiviswanathan et al, 2013). Further, the popularity of MLPs has meant that subsequent research has continued to use MLPs (He and Valeo, 2009;Napolitano et al, 2011) and thus form a reference for the basis of comparing ANN performance (Alvisi and Franchini, 2011).…”

Section: Fuzzy Neural Network 231 Background On Artificial Neural mentioning

confidence: 99%

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Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan

Valeo

2016

Hydrol. Earth Syst. Sci.

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Abstract.A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

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Section: Fuzzy Numbers and Data-driven Modellingmentioning

confidence: 99%

Section: Fuzzy Neural Network 231 Background On Artificial Neural mentioning

confidence: 99%

Section: Fuzzy Neural Network 231 Background On Artificial Neural mentioning

confidence: 99%

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Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan

Valeo

2016

Hydrol. Earth Syst. Sci.

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“…The candidate site with the highest R value was designated as the associate site for a given target site. This approach in which MI is taken into account in the selection of model inputs has been employed in previous studies (Talei et al, 2010;Elshorbagy et al, 2010;He et al, 2011). Table 4 lists the event-averaged CCs between water-level data from each target site and their candidate sites, as well as the event-averaged MI of the first input variable (i.e., identified cumulative rainfall) of the target site and the waterlevel data from the candidate sites.…”

Section: Determination Of Input Variablesmentioning

confidence: 99%

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

Ouyang

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multiobjective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four waterlevel gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

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“…With the rapid development of computational capability, data-driven machine learning methods have become more popular in the past decade in all fields related to data and modeling, including hydrology (e.g., [1][2][3][4][5][6][7]). Unlike typical hydrologic models, data-driven approaches do not rely directly on explicit physical knowledge of the target process.…”

Section: Introductionmentioning

confidence: 99%

Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches

Chang

Chen

2018

Water

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Data-driven machine learning approaches have been rapidly developed in the past 10 to 20 years and applied to various problems in the field of hydrology. To investigate the capability of data-driven approaches in rainfall-runoff modeling in comparison to theory-driven models, we conducted a comparative study of simulated monthly surface runoff at 203 watersheds across the contiguous USA using a conceptual model, the proportionality hydrologic model, and a data-driven Gaussian process regression model. With the same input variables of precipitation and mean monthly aridity index, the two models showed similar performance. We then introduced two more input variables in the data-driven model: potential evaporation and the normalized difference vegetation index (NDVI), which were selected based on hydrologic knowledge. The modified data-driven model performed much better than either the conceptual or original data-driven model. A sensitivity analysis was conducted on all three models tested in this study, which showed that surface runoff responded positively to increased precipitation. However, a confounding effect on surface runoff sensitivity was found among mean monthly aridity index, potential evaporation, and NDVI. This confounding was caused by complex interconnections among energy supply, vegetation coverage, and climate seasonality of the watershed system. We also conducted an uncertainty analysis on the two data-driven models, which showed that both models had reasonable predictability within the 95% confidence interval. With the additional two input variables, the modified data-driven model had lower prediction uncertainty and higher prediction accuracy.

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Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

Cited by 174 publications

References 46 publications

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches

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