The major task for the machine learning algorithms in this study is presented in the upper part of the figure: To estimate the unknown tracer concentration, C, by training a machine learning algorithm to the pattern formed by a subset of discharge and a measured pair of tracers. The structure of the chosen algorithm for this study are shown in subplots a, b, c and d.

The major task for the machine learning algorithms in this study is presented in the upper part of the figure: To estimate the unknown tracer concentration, C, by training a machine learning algorithm to the pattern formed by a subset of discharge and a measured pair of tracers. The structure of the chosen algorithm for this study are shown in subplots a, b, c and d.

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Karstic groundwater systems are often investigated by a combination of environmental or artificial tracers. One of the major downsides of tracer‐based methods is the limited availability of tracer measurements, especially in data sparse regions. This study presents an approach to systematically evaluate the information content of the available data...

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... T. Yang et al., 2016), water quality prediction (K. Chen et al., 2020;Lu & Ma, 2020;, data mining for sparse environmental data measurement (Mewes et al., 2020;Zhou, 2020), evapotranspiration estimation (Goyal et al., 2014;, groundwater management (Naghibi & Pourghasemi, 2016;Podgorski & Berg, 2020), and so on. It has been confirmed that machine learning is an effective tool to explore implicit relationships in complex nonlinear systems (Goodfellow et al., 2016;Lecun et al., 2015). ...
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... Machine learning models are increasingly used to make hydrological predictions [71,72], and the most accurate versions tend to utilize ensemble models that combine inputs from independent algorithms before making final decisions [73][74][75]. Machine learning models can also be used to explore complex, non-linear relationships between predictor and target variables. ...
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... Considering the recent wide applicability of "shallow" neural networks in hydrology, oceanology and atmospheric sciences (Crawford et al., 2019;Bergen et al., 2019;Berkhahn et al., 2019;Kulp and Strauss, 2019;Sezen et al., 2019;Flombaum et al., 2020;Jia et al., 2020;Diez-Sierra and del Jesus, 2020;Nourani et al., 2020;Mewes et al., 2020;Snieder et al., 2020), we hope the proposed method may be beneficial not only for stream temperature modelling, but possibly also other hydrological applications. ...
Article
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... High-frequency conductivity measurements were effective predictors of all major ions derived from weathering of mountaintop removal mined watersheds (Ross et al., 2018). High-frequency sulphate time series were produced with discharge as an input variable for multiple machine learning algorithms (Mewes et al., 2020). Kisi and Parmar (2016) predicted monthly chemical oxygen demand in an Indian river with nutrient and other water quality information. ...
... We accepted default parameters for the RF model, including the number of trees required for the ensemble (n = 500) and the number of variables tried at each split in an individual tree (mtry = 2). We chose the SVM and RF models because both have been previously applied in hydrological contexts with strong results (e.g., Kim et al., 2020;Mewes et al., 2020). The main difference between the two is the RF uses discrete predictions, which can help identify non-linear patterns, and the SVM is a continuous function. ...
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... hervorgehobene Nützlichkeit von DDMs als Unterstützung von konzeptionellen Modellen. Beispielsweise als Kombination der Modellstrukturen zur Vorhersage einer Zielgröße, bspw.Ratto et al. (2007), oder als Verwendung von ML zur Aufbereitung und Validierung von Eingangsdaten(Mewes, Oppel & Hartmann, 2019; für konzeptionelle Modelle. Unabhängig von bestehenden Konzepten wird allgemein empfohlen so viele Informationen über das Zielgebiet wie möglich in die gewählte Modellstruktur einzubeziehen. ...
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In data scarce regions common hydrological models cannot be applied. Due to the missing data for calibration conceptual rainfall-runoff models cannot be parametrized and, hence, not be used for operational predictions or definition of design hydrographs. Geomorphological instantaneous unit hydrographs (GIUH) offer the unique possibility to adapt model structure to catchment structure and thereby increase model accuracy in data scarce regions. The parsimony as well as the incorporation of catchment structures is a valuable advantage for prediction in ungauged basins. The drawback of GIUH-models is the required parametrization for each individual event. Hence, applications of GIUH-models have been limited to scientific reanalysis of past rainfall-runoff events. In this study an ensemble of machine learning (ML) algorithms was applied for the estimation of the required parameters in ungauged basins. Indicators of meteorological forcing and initial catchments states were used as predictors for the estimation of drainage velocity and runoff coefficient. Eight algorithms were applied and their performance has been evaluated in a leave-one-out study in three major catchments in South-East Germany. Predictions provided by the algorithms were given to an improved GIUH-model to transform 2-dimensional precipitation data into an ensemble prediction of hydrographs in ungauged basins. The performance of the improved GIUH-model and the ML-Algorithms were evaluated separately. The GIUH-structure proved to be as flexible as demanded. In a synthetic case study it was able to incorporate different catchment shapes, flowpath distributions and characteristics into the shape of predicted hydrographs. A variation of drainage velocity by flowpath was implemented and improved simulation results. Moreover, a parametrization directly from rainfall-runoff event analysis seemed possible, yet calibrated parameters led to better performance. The setup of the ML-module has been evaluated with respect to the predictors and data segmentation by model approach. In a subsequent regional application, data from all available gauges were used to train the algorithms. Withheld data was used to imitate a prediction in ungauged basin. The models showed an average relative error for drainage velocity of 20% and 40% for runoff volume. The error were lower afterwards by selective data composition, considering only a limited number of similar catchments for model training. The combination of both model components were tested subsequently. The mean efficiencies considering hydrograph timing, volume and variance were close to optimum value. Yet the model worked only in ensemble mode, because a single ML-algorithms proved not to be capable of imitating the full range of hydrological complexity. A comparison to a regionalized HBV-model showed superior results for the GIUH-ML model in ungauged catchments and equal results for gauged catchments. Finally, the possibility of deriving assumptions about hydrological processes from trained ML-dependencies has been discussed. For the performed case studies an assumption about changing dependencies of driving factors and the resulting ratio of flood volume and peak was derived.
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Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC-HMS) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research’s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. Furthermore, we also performed a hydraulic simulation in Hydrological Engineering Center—Geospatial River Analysis System (HEC-RAS) using the input discharge obtained from the Random Forest model. The reliability of the Random Forest model and the HEC-HMS model was evaluated using different statistical indexes. The coefficient of determination (R2), standard deviation ratio (RSR), and normalized root mean square error (NRMSE) were 0.94, 0.23, and 0.17 for the training data and 0.72, 0.56, and 0.26 for the testing data, respectively, for the Random Forest model. Similarly, the R2, RSR, and NRMSE were 0.99, 0.16, and 0.06 for the calibration period and 0.96, 0.35, and 0.10 for the validation period, respectively, for the HEC-HMS model. The Random Forest model slightly underestimated peak discharge values, whereas the HEC-HMS model slightly overestimated the peak discharge value. Statistical index values illustrated the good performance of the Random Forest and HEC-HMS models, which revealed the suitability of both models for hydrology analysis. In addition, the flood depth generated by HEC-RAS using the Random Forest predicted discharge underestimated the flood depth during the peak flooding event. This result proves that HEC-HMS could compensate Random Forest for the peak discharge and flood depth during extreme events. In conclusion, the integrated machine learning and physical-based model can provide more confidence in rainfall-runoff and flood depth prediction.
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Statistical learning methods offer a promising approach for low-flow regionalization. We examine seven statistical learning models (Lasso, linear, and nonlinear-model-based boosting, sparse partial least squares, principal component regression, random forest, and support vector regression) for the prediction of winter and summer low flow based on a hydrologically diverse dataset of 260 catchments in Austria. In order to produce sparse models, we adapt the recursive feature elimination for variable preselection and propose using three different variable ranking methods (conditional forest, Lasso, and linear model-based boosting) for each of the prediction models. Results are evaluated for the low-flow characteristic Q95 (Pr(Q>Q95)=0.95) standardized by catchment area using a repeated nested cross-validation scheme. We found a generally high prediction accuracy for winter (RCV2 of 0.66 to 0.7) and summer (RCV2 of 0.83 to 0.86). The models perform similarly to or slightly better than a top-kriging model that constitutes the current benchmark for the study area. The best-performing models are support vector regression (winter) and nonlinear model-based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all the models with only a small loss of performance. The so-obtained learning models are more parsimonious and thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and nonlinear models reveals that nonlinear processes can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add nonlinear effects. When performing low-flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.
Chapter
Hydrology is the science of studying the natural flow of water and the effect of human activity on the water. Hydrological modeling is essential for the management and conservation of water. In recent decades, machine learning (ML) has been applied efficiently in hydrology. In this study, the application of ML in four subfields of hydrology, including flood, precipitation estimation, water quality, and groundwater, is presented. This review shows that ML performs better in flood prediction than traditional data-driven and physical hydrology modeling, particularly in short-term flood forecasting. In addition, using the ML technique helps to estimate precipitation from satellite datasets. This study provides a review of the potential of ML in water quality and groundwater modeling. The study shows that using an optimization algorithm for parameter selection can improve the performance of ML. Moreover, modeling accuracy is often improved through ML hybridization. Finally, it is recommended that hydrologists use ML in their modeling owing to their low computational cost and high performance.
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Statistical learning methods offer a promising approach for low flow regionalization. We examine seven statistical learning models (lasso, linear and non-linear model based boosting, sparse partial least squares, principal component regression, random forest, and support vector machine regression) for the prediction of winter and summer low flow based on a hydrological diverse dataset of 260 catchments in Austria. In order to produce sparse models we adapt the recursive feature elimination for variable preselection and propose to use three different variable ranking methods (conditional forest, lasso and linear model based boosting) for each of the prediction models. Results are evaluated for the low flow characteristic Q95 (Pr(Q>Q95) = 0.95) standardized by catchment area using a repeated nested cross validation scheme. We found a generally high prediction accuracy for winter (R2CV of 0.66 to 0.7) and summer (R2CV of 0.83 to 0.86). The models perform similar or slightly better than a Top-kriging model that constitutes the current benchmark for the study area. The best performing models are support vector machine regression (winter) and non-linear model based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all models with only a small loss of performance. The so obtained learning models are more parsimonious, thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and non-linear models reveals that non-linear relationships can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add non-liner effects. When performing low flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.