Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments

Mewes, Benjamin; Oppel, Henning; Marx, Vera; Hartmann, Andreas

doi:10.1029/2018wr024558

Cited by 15 publications

(12 citation statements)

References 59 publications

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“…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.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Green

Pardo

Bailey

et al. 2020

Hydrological Processes

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Stream solute monitoring has produced many insights into ecosystem and Earth system functions. Although new sensors have provided novel information about the fine-scale temporal variation of some stream water solutes, we lack adequate sensor technology to gain the same insights for many other solutes. We used two machine learning algorithms-Support Vector Machine and Random Forestto predict concentrations at 15-min resolution for 10 solutes, of which eight lack specific sensors. The algorithms were trained with data from intensive stream sensing and manual stream sampling (weekly) for four full years in a hydrologic reference stream within the Hubbard Brook Experimental Forest in New Hampshire, USA. The Random Forest algorithm was slightly better at predicting solute concentrations than the Support Vector Machine algorithm (Nash-Sutcliffe efficiencies ranged from 0.35 to 0.78 for Random Forest compared to 0.29 to 0.79 for Support Vector Machine). Solute predictions were most sensitive to the removal of fluorescent dissolved organic matter, pH and specific conductance as independent variables for both algorithms, and least sensitive to dissolved oxygen and turbidity. The predicted concentrations of calcium and monomeric aluminium were used to estimate catchment solute yield, which changed most dramatically for aluminium because it concentrates with stream discharge. These results show great promise for using a combined approach of stream sensing and intensive stream discrete sampling to build information about the highfrequency variation of solutes for which an appropriate sensor or proxy is not available.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Green

Pardo

Bailey

et al. 2020

Hydrological Processes

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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.…”

Section: Using Machine Learning To Predict Nutrient Concentration and Fluxmentioning

confidence: 99%

Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources

et al. 2021

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Human agriculture, wastewater, and use of fossil fuels have saturated ecosystems with nitrogen and phosphorus, threatening biodiversity and human water security at a global scale. Despite efforts to reduce nutrient pollution, carbon and nutrient concentrations have increased or remained high in many regions. Here, we applied a new ecohydrological framework to ~12,000 water samples collected by the U.S. Environmental Protection Agency from streams and lakes across the contiguous U.S. to identify spatial and temporal patterns in nutrient concentrations and leverage (an indicator of flux). For the contiguous U.S. and within ecoregions, we quantified trends for sites sampled repeatedly from 2000 to 2019, the persistence of spatial patterns over that period, and the patch size of nutrient sources and sinks. While we observed various temporal trends across ecoregions, the spatial patterns of nutrient and carbon concentrations in streams were persistent across and within ecoregions, potentially because of historical nutrient legacies, consistent nutrient sources, and inherent differences in nutrient removal capacity for various ecosystems. Watersheds showed strong critical source area dynamics in that 2–8% of the land area accounted for 75% of the estimated flux. Variability in nutrient contribution was greatest in catchments smaller than 250 km2 for most parameters. An ensemble of four machine learning models confirmed previously observed relationships between nutrient concentrations and a combination of land use and land cover, demonstrating how human activity and inherent nutrient removal capacity interactively determine nutrient balance. These findings suggest that targeted nutrient interventions in a small portion of the landscape could substantially improve water quality at continental scales. We recommend a dual approach of first prioritizing the reduction of nutrient inputs in catchments that exert disproportionate influence on downstream water chemistry, and second, enhancing nutrient removal capacity by restoring hydrological connectivity both laterally and vertically in stream networks.

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“…ML provides a solution to a real-world problem by studying previously observed data and has been effective in generating accurate results [28]. ML provides adequate computation power [29,30] and is used in a wide variety of research and applications in hydrology. Some examples of ML applications in the hydrology domain are rainfall-runoff prediction [31][32][33], flood forecasting [34][35][36], sedimentation studies [37][38][39], water quality prediction [40][41][42][43], groundwater prediction [44,45], river temperature prediction [46][47][48][49], and rainfall estimation [50,51].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

et al. 2022

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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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Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments

Cited by 15 publications

References 59 publications

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

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