Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).
Google Scholar
Turing, A. M. On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. s2-42, 230–265 (1937).
Google Scholar
Muggleton, S. Alan Turing and the development of artificial intelligence. AI Commun. 27, 3–10 (2014).
Google Scholar
Dechter, R. Learning while searching in constraint-satisfaction problems. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 5, 178–183 (AAAI, 1986).
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).
Google Scholar
Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).
Google Scholar
Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).
Google Scholar
Shen, C. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour. Res. 54, 8558–8593 (2018).
Google Scholar
Shen, C. P. et al. HESS opinions: incubating deep-learning-powered hydrologic science advances as a community. Hydrol. Earth Syst. Sci. 22, 5639–5656 (2018).
Google Scholar
Xu, T. & Liang, F. Machine learning for hydrologic sciences: an introductory overview. Wiley Interdiscip. Rev. Water 8, e1533 (2021).
Google Scholar
Zhi, W. et al. From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environ. Sci. Technol. 55, 2357–2368 (2021).
Google Scholar
Varadharajan, C. et al. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? Hydrol. Process. https://doi.org/10.1002/hyp.14565 (2022).
Tripathy, K. P. & Mishra, A. K. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2023.130458 (2023).
Perry, G. L. W., Seidl, R., Bellvé, A. M. & Rammer, W. An outlook for deep learning in ecosystem science. Ecosystems 25, 1700–1718 (2022).
Google Scholar
Song, T. et al. A review of artificial intelligence in marine science. Front. Earth Sci. https://doi.org/10.3389/feart.2023.1090185 (2023).
Sun, A. Y. & Scanlon, B. R. How can big data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environ. Res. Lett. 14, 073001 (2019).
Google Scholar
Zhu, J.-J., Yang, M. & Ren, Z. J. Machine learning in environmental research: common pitfalls and best practices. Environ. Sci. Technol. 57, 17671–17689 (2023).
Google Scholar
van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020).
Google Scholar
Zhong, S. et al. Machine learning: new ideas and tools in environmental science and engineering. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.1c01339 (2021).
Appling, A. P., Oliver, S. K., Read, J. S., Sadler, J. M. & Zwart, J. A. in Encyclopedia of Inland Waters 2nd edn (eds Mehner, T. & Tockner, K.) 585–606 (Elsevier, 2022).
Shen, C. et al. Differentiable modelling to unify machine learning and physical models for geosciences. Nat. Rev. Earth Environ. 4, 552–567 (2023).
Diamond, J. S. et al. Hypoxia is common in temperate headwaters and driven by hydrological extremes. Ecol. Indic. 147, 109987 (2023).
Google Scholar
Creed, I. F. et al. Enhancing protection for vulnerable waters. Nat. Geosci. 10, 809–815 (2017).
Google Scholar
Nazari-Sharabian, M., Ahmad, S. & Karakouzian, M. Climate change and eutrophication: a short review. Eng. Technol. Appl. Sci. Res. 8, 3668 (2018).
Google Scholar
Paerl, H. W., Otten, T. G. & Kudela, R. Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum. Environ. Sci. Technol. 52, 5519–5529 (2018).
Google Scholar
McCabe, M. F. et al. The future of Earth observation in hydrology. Hydrol. Earth Syst. Sci. 21, 3879–3914 (2017).
Google Scholar
Abbott, B. W. et al. Using multi-tracer inference to move beyond single-catchment ecohydrology. Earth Sci. Rev. 160, 19–42 (2016).
Google Scholar
Milly, P. C. D. et al. Stationarity is dead: whither water management? Science 319, 573–574 (2008).
Google Scholar
Runkel, R. L., Crawford, C. G. & Cohn, T. A. Load Estimator (LOADEST): A FORTRAN Program for Estimating Constituent Loads in Streams and Rivers Report no. 4-A5 (USGS, 2004).
Hirsch, R. M., Moyer, D. L. & Archfield, S. A. Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay River inputs. J. Am. Water Resour. Assoc. 46, 857–880 (2010).
Google Scholar
Zhang, Q., Blomquist, J. D., Moyer, D. L. & Chanat, J. G. Estimation bias in water-quality constituent concentrations and fluxes: a synthesis for Chesapeake Bay rivers and streams. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00109 (2019).
Zhi, W. et al. Distinct source water chemistry shapes contrasting concentration–discharge patterns. Water Resour. Res. 55, 4233–4251 (2019).
Google Scholar
Archfield, S. A. et al. Accelerating advances in continental domain hydrologic modeling. Water Resour. Res. 51, 10078–10091 (2015).
Google Scholar
Zhi, W. et al. BioRT-Flux-PIHM v1.0: a watershed biogeochemical reactive transport model. Geosci. Model Dev. 15, 19 (2022).
Google Scholar
Rahmani, F. et al. Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/abd501 (2021).
Bolton, T. & Zanna, L. Applications of deep learning to ocean data inference and subgrid parameterization. J. Adv. Model. Earth Syst. 11, 376–399 (2019).
Google Scholar
Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).
Google Scholar
Irrgang, C. et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3, 667–674 (2021).
Google Scholar
Zhi, W., Ouyang, W., Shen, C. & Li, L. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers. Nat. Water 1, 249–260 (2023).
Google Scholar
Willard, J. D. et al. Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning. Water Resour. Res. 57, e2021WR029579 (2021).
Google Scholar
He, Y. et al. Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images. Water Res. 215, 118241 (2022).
Google Scholar
Jia, X. et al. Physics-guided recurrent graph model for predicting flow and temperature in river networks. In Proc. 2021 SIAM International Conference on Data Mining (SDM) 612–620 (SIAM, 2021); https://doi.org/10.1137/1.9781611976700.69
Bao, T. et al. Partial differential equation driven dynamic graph networks for predicting stream water temperature. In 2021 IEEE International Conference on Data Mining (ICDM) 11–20 (IEEE, 2021); https://doi.org/10.1109/ICDM51629.2021.00011
Bindas, T. et al. Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning. Water Resour. Res. 60, e2023WR035337 (2024).
Chen, S. et al. Heterogeneous stream-reservoir graph networks with data assimilation. In 2021 IEEE International Conference on Data Mining (ICDM) 1024–1029 (IEEE, 2021).
Chen, S., Zwart, J. A. & Jia, X. Physics-guided graph meta learning for predicting water temperature and streamflow in stream networks. In Proc. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2752–2761 (Association for Computing Machinery, 2022).
Saha, G. K., Rahmani, F., Shen, C., Li, L. & Cibin, R. A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds. Sci. Total Environ. 878, 162930 (2023).
Google Scholar
Kratzert, F. et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol. Earth Syst. Sci. 23, 5089–5110 (2019).
Google Scholar
Willard, J. D., Read, J. S., Topp, S., Hansen, G. J. A. & Kumar, V. Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020). Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10249 (2022).
Ren, H., Cromwell, E., Kravitz, B. & Chen, X. Using long short-term memory models to fill data gaps in hydrological monitoring networks. Hydrol. Earth Syst. Sci. 26, 1727–1743 (2022).
Google Scholar
Latif, S. D. et al. Sediment load prediction in Johor River: deep learning versus machine learning models. Appl. Water Sci. https://doi.org/10.1007/s13201-023-01874-w (2023).
Jamei, M. et al. Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting. Ecol. Indic. 153, 110478 (2023).
Google Scholar
Hill, P. R., Kumar, A., Temimi, M. & Bull, D. R. HABNet: machine learning, remote sensing-based detection of harmful algal blooms. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3229–3239 (2020).
Google Scholar
D’Alimonte, D., Zibordi, G. & Berthon, J. F. Determination of CDOM and NPPM absorption coefficient spectra from coastal water remote sensing reflectance. IEEE Trans. Geosci. Remote Sens. 42, 1770–1777 (2004).
Google Scholar
Zhang, Y. et al. Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods. GIsci. Remote Sens. 59, 1367–1383 (2022).
Google Scholar
Li, L. et al. River water quality shaped by land–river connectivity in a changing climate. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01923-x (2024).
Rasmussen, P. P., Gray, J. R., Glysson, G. D. & Ziegler, A. C. Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Report No. 3-C4 (USGS, 2009).
Covert, S. A., Bunch, A. R., Crawford, C. G. & Oelsner, G. P. Comparison of Surrogate Models to Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites during Water Years 2013–18 Report No. 2022-5109 (USGS, 2023).
Schilling, K. E., Kim, S.-W. & Jones, C. S. Use of water quality surrogates to estimate total phosphorus concentrations in Iowa rivers. J. Hydrol. Reg. Stud. 12, 111–121 (2017).
Google Scholar
Wang, D. et al. Satellite retrieval of surface water nutrients in the coastal regions of the East China Sea. Remote Sens. 10, 1896 (2018).
Google Scholar
Guo, H. et al. Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive. ISPRS J. Photogramm. Remote Sens. 183, 451–469 (2022).
Google Scholar
Kerins, D. & Li, L. High dissolved carbon concentration in arid rocky mountain streams. Environ. Sci. Technol. 57, 4656–4667 (2023).
Google Scholar
Zhang, Y. et al. A hybrid model combining mode decomposition and deep learning algorithms for detecting TP in urban sewer networks. Appl. Energy 333, 120600 (2023).
Google Scholar
Jiang, Y., Li, C., Song, H. & Wang, W. Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks. J. Hazard. Mater. https://doi.org/10.1016/j.jhazmat.2022.128732 (2022).
Li, L. et al. Expanding the role of reactive transport models in critical zone processes. Earth Sci. Rev. 165, 280–301 (2017).
Google Scholar
Li, L. et al. Toward catchment hydro-biogeochemical theories. WIREs Water 8, e1495 (2021).
Google Scholar
Bergen, K. J., Johnson, P. A., de Hoop, M. V. & Beroza, G. C. Machine learning for data-driven discovery in solid Earth geoscience. Science 363, eaau0323 (2019).
Google Scholar
Kolbe, T. et al. Stratification of reactivity determines nitrate removal in groundwater. Proc. Natl Acad. Sci. USA 116, 2494–2499 (2019).
Google Scholar
Sun, A. Y. Discovering state‐parameter mappings in subsurface models using generative adversarial networks. Geophys. Res. Lett. https://doi.org/10.1029/2018gl080404 (2018).
Jiang, P., Shuai, P., Sun, A., Mudunuru, M. K. & Chen, X. Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado. Hydrol. Earth Syst. Sci. Discuss. 2022, 1–31 (2022).
Jiang, Z. et al. Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping. Geosci. Model Dev. 14, 3421–3435 (2021).
Google Scholar
Cromwell, E. et al. Estimating watershed subsurface permeability from stream discharge data using deep neural networks. Front. Earth Sci. https://doi.org/10.3389/feart.2021.613011 (2021).
Podgorski, J. & Berg, M. Global threat of arsenic in groundwater. Science 368, 845–850 (2020).
Google Scholar
Podgorski, J. & Berg, M. Global analysis and prediction of fluoride in groundwater. Nat. Commun. https://doi.org/10.1038/s41467-022-31940-x (2022).
Ransom, K. M., Nolan, B. T., Stackelberg, P. E., Belitz, K. & Fram, M. S. Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States. Sci. Total Environ. 807, 151065 (2022).
Google Scholar
Nolan, B. T., Green, C. T., Juckem, P. F., Liao, L. & Reddy, J. E. Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA. J. Hydrol. 559, 428–441 (2018).
Google Scholar
Wen, T., Liu, M., Woda, J., Zheng, G. & Brantley, S. L. Detecting anomalous methane in groundwater within hydrocarbon production areas across the United States. Water Res. 200, 117236 (2021).
Google Scholar
Willard, J., Jia, X., Xu, S., Steinbach, M. & Kumar, V. Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Comput. Surv. 55, 1–37 (2023).
Google Scholar
Feng, D., Beck, H., Lawson, K. & Shen, C. The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment. Hydrol. Earth Syst. Sci. Discuss. 2022, 1–28 (2022).
Sun, A. Y., Yoon, H., Shih, C.-Y. & Zhong, Z. Applications of physics-informed scientific machine learning in subsurface science: A survey. In Knowledge Guided Machine Learning (eds Karpatne, A. et al.) 111–132 (Chapman and Hall/CRC, 2022).
Read, J. S. et al. Process‐guided deep learning predictions of lake water temperature. Water Resour. Res. 55, 9173–9190 (2019).
Google Scholar
Beucler, T., Rasp, S., Pritchard, M. & Gentine, P. Achieving conservation of energy in neural network emulators for climate modeling. Preprint at https://arxiv.org/abs/1906.06622 (2019).
Daw, A. et al. Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling. In Proc. 2020 SIAM International Conference on Data Mining (SDM) 532–540 (SIAM, 2020).
Shen, C. et al. Differentiable modelling to unify machine learning and physical models for geosciences. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-023-00450-9 (2023).
Jia, X. et al. Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles. In Proceedings of the 2019 SIAM International Conference on Data Mining 558–566 (SIAM, 2019).
Sadler, J. M. et al. Multi-task deep learning of daily streamflow and water temperature. Water Resour. Res. 58, e2021WR030138 (2022).
Google Scholar
He, Q., Barajas-Solano, D., Tartakovsky, G. & Tartakovsky, A. M. Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport. Adv. Water Res. 141, 103610 (2020).
Google Scholar
Jiang, S., Zheng, Y. & Solomatine, D. Improving AI system awareness of geoscience knowledge: symbiotic integration of physical approaches and deep learning. Geophys. Res. Lett. https://doi.org/10.1029/2020gl088229 (2020).
Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C. & Fenicia, F. Improving hydrologic models for predictions and process understanding using neural ODEs. Hydrol. Earth Syst. Sci. 26, 5085–5102 (2022).
Google Scholar
Molnar, C. Interpretable Machine Learning (Lulu.com, 2020).
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J. & Muller, K.-R. Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109, 247–278 (2021).
Google Scholar
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In International Conference on Machine Learning 3319–3328 (ICML, 2017).
Erion, G. et al. Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 3, 620–631 (2021).
Google Scholar
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 1–10 (2017).
Ribeiro, M. T., Singh, S. & Guestrin, C. ” Why should i trust you?” Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).
Xie, W. et al. Interpretable framework of physics‐guided neural network with attention mechanism: simulating paddy field water temperature variations. Water Resour. Res. 58, e2021WR030493 (2022).
Google Scholar
Liu, Y., Duffy, K., Dy, J. G. & Ganguly, A. R. Explainable deep learning for insights in El Niño and river flows. Nat. Commun. https://doi.org/10.1038/s41467-023-35968-5 (2023).
Sadayappan, K., Kerins, D., Shen, C. & Li, L. Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers. Water Res. 226, 119295 (2022).
Google Scholar
Topp, S. N. et al. Stream temperature prediction in a shifting environment: explaining the influence of deep learning architecture. Water Resour. Res. https://doi.org/10.1029/2022wr033880 (2023).
Lee, D. et al. Integrated explainable deep learning prediction of harmful algal blooms. Technol. Forecast. Soc. Change 185, 122046 (2022).
Google Scholar
Zheng, H., Liu, Y., Wan, W., Zhao, J. & Xie, G. Large-scale prediction of stream water quality using an interpretable deep learning approach. J. Environ. Manage. 331, 117309 (2023).
Google Scholar
Hanson, P. C. et al. Predicting lake surface water phosphorus dynamics using process-guided machine learning. Ecol. Modell. 430, 109136 (2020).
Google Scholar
Carpenter, S. R., Booth, E. G. & Kucharik, C. J. Extreme precipitation and phosphorus loads from two agricultural watersheds. Limnol. Oceanogr. 63, 1221–1233 (2018).
Google Scholar
Robinne, F.-N. et al. Scientists’ warning on extreme wildfire risks to water supply. Hydrol. Process. 35, e14086 (2021).
Google Scholar
Whitehead, P. G., Wilby, R. L., Battarbee, R. W., Kernan, M. & Wade, A. J. A review of the potential impacts of climate change on surface water quality. Hydrol. Sci. J. 54, 101–123 (2009).
Google Scholar
Wang, P. et al. Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Sci. Total Environ. 693, 133440 (2019).
Google Scholar
Kontos, Y. N., Kassandros, T., Katsifarakis, K. L. & Karatzas, K. Deep Learning Modeling of Groundwater Pollution Sources. In International Conference on Engineering Applications of Neural Networks 165–177 (Springer, 2021).
Zwart, J. A. et al. Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions. J. Am. Water Res. Assoc. 59, 317–337 (2023).
Google Scholar
van Lieshout, C., van Oeveren, K., van Emmerik, T. & Postma, E. Automated river plastic monitoring using deep learning and cameras. Earth Space Sci. 7, e2019EA000960 (2020).
Google Scholar
Beria, H., Larsen, J. R., Michelon, A., Ceperley, N. C. & Schaefli, B. HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources. Geosci. Model Dev. 13, 2433–2450 (2020).
Google Scholar
Tang, Y., Reed, P. & Wagener, T. How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci. 10, 289–307 (2006).
Google Scholar
Iman, M., Arabnia, H. R. & Rasheed, K. A review of deep transfer learning and recent advancements. Technologies 11, 40 (2023).
Google Scholar
Qian, K., Jiang, J., Ding, Y. & Yang, S.-H. DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification. Neural Comput. Appl. 33, 11889–11903 (2021).
Google Scholar
Chen, Z. et al. A transfer learning-based LSTM strategy for imputing large-scale consecutive missing data and its application in a water quality prediction system. J. Hydrol. 602, 126573 (2021).
Google Scholar
Xiang, Z., Yan, J. & Demir, I. A rainfall-runoff model with LSTM-based sequence-to-sequence learning. Water Resour. Res. 56, e2019WR025326 (2020).
Google Scholar
Nearing, G. S. et al. What role does hydrological science play in the age of machine learning? Water Resour. Res. https://doi.org/10.1029/2020WR028091 (2021).
Guo, D. et al. A data-based predictive model for spatiotemporal variability in stream water quality. Hydrol. Earth Syst. Sci. 24, 827–847 (2020).
Google Scholar
Zimmer, M. A. et al. Zero or not? Causes and consequences of zero-flow stream gage readings. WIREs Water 7, e1436 (2020).
Google Scholar
Chaudhary, P., D’Aronco, S., Moy de Vitry, M., Leitão, J. P. & Wegner, J. D. Flood-water level estimation from social media images. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. https://doi.org/10.5194/isprs-annals-IV-2-W5-5-2019 (2019).
Kanth, A. K., Chitra, P. & Sowmya, G. G. Deep learning-based assessment of flood severity using social media streams. Stoch. Environ. Res. Risk Assess. 36, 473–493 (2022).
Google Scholar
Hanif, M., Khawar, A., Tahir, M. A. & Rafi, M. Deep learning based framework for classification of water quality in social media data. In Proc. MediaEval 2021 Workshop (MediaEval 2021).
Njue, N. et al. Citizen science in hydrological monitoring and ecosystem services management: state of the art and future prospects. Sci. Total Environ. 693, 133531 (2019).
Google Scholar
Yevenes, M. A., Pereira, H. & Bermudez, R. Citizen science as a co-creative measure to water quality: chemical data and local participation in a rural territory. Front. Environ. Sci. 10, 940778 (2022).
Google Scholar
Nardi, F. et al. Citizens and Hydrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges. Hydrol. Sci. J. 67, 2534–2551 (2022).
Google Scholar
Dyer, F. et al. Waterwatch data quality: an opportunity to augment professionally collected data sets. In Proc. 7th Australian Stream Management Conference 27–30 (ASM, 2014).
Rose, L. A., Karwan, D. L. & Godsey, S. E. Concentration–discharge relationships describe solute and sediment mobilization, reaction, and transport at event and longer timescales. Hydrol. Processes 32, 2829–2844 (2018).
Google Scholar
Hare, D. K., Helton, A. M., Johnson, Z. C., Lane, J. W. & Briggs, M. A. Continental-scale analysis of shallow and deep groundwater contributions to streams. Nat. Commun. https://doi.org/10.1038/s41467-021-21651-0 (2021).
Li, L. et al. Toward catchment hydro‐biogeochemical theories. WIREs Water https://doi.org/10.1002/wat2.1495 (2021).
Zhi, W. & Li, L. The shallow and deep hypothesis: subsurface vertical chemical contrasts shape nitrate export patterns from different land uses. Environ. Sci. Technol. 54, 11915–11928 (2020).
Google Scholar
Zhi, W., Klingler, C., Liu, J. & Li, L. Widespread deoxygenation in warming rivers. Nat. Clim. Change 13, 1105–1113 (2023).
Google Scholar
Li, L. et al. Climate controls on river chemistry. Earths Future 10, e2021EF002603 (2022).
Google Scholar
Harari, Y. N. Sapiens: A Brief History of Humankind (Random House, 2014).
Popper, K. The Logic of Scientific Discovery (Basic Books, 1959).
Hodges, A. Alan Turing: The Enigma: The Centenary Edition (Princeton Univ. Press, 2012).
Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)—Part 1: the production of a daily streamflow archive and metadata. Earth Syst. Sci. Data 10, 765–785 (2018).
Google Scholar
Virro, H., Amatulli, G., Kmoch, A., Shen, L. & Uuemaa, E. GRQA: global river water quality archive. Earth System Sci. Data 13, 5483–5507 (2021).
Gunn, M. A., Matherne, A. M. & Mason, J. R. R. The USGS at Embudo, New Mexico: 125 Years of Systematic Streamgaging in the United States Report No. 2014-30344 (USGS, 2014).
Burt, T. P. & McDonnell, J. J. Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses. Water Resour. Res. 51, 5919–5928 (2015).
Google Scholar
Read, E. K. et al. Water quality data for national‐scale aquatic research: the Water Quality Portal. Water Resour. Res. 53, 1735–1745 (2017).
Google Scholar
Council, N. R. Confronting the Nation’s Water Problems: The Role of Research (National Academies Press, 2004).
Li, Z., Liu, H., Zhang, C. & Fu, G. Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring. Environ. Sci. Ecotechnol. 14, 100231 (2023).
Google Scholar
Qu, H. & Yuan, W. Water quality Anomaly detection based on optimally reconfigured convolutional autoencoder. In 2022 International Conference on Wearables, Sports and Lifestyle Management (WSLM) 137–141 (IEEE, 2022).
Shen, C., Chen, X. & Laloy, E. Broadening the use of machine learning in hydrology. Front. Water https://doi.org/10.3389/frwa.2021.681023 (2021).
Schmidhuber, J. Annotated history of modern AI and deep learning. Preprint at https://arxiv.org/abs/2212.11279 (2022).
McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).
Google Scholar
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958).
Google Scholar
Amari, S.-I. Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Trans. Comput. 100, 1197–1206 (1972).
Google Scholar
Maier, H. R. & Dandy, G. C. The use of artificial neural networks for the prediction of water quality parameters. Water Resour. Res. 32, 1013–1022 (1996).
Google Scholar
Maier, H. R. & Dandy, G. C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modell. Softw. 15, 101–124 (2000).
Google Scholar
Chang, F. J. & Hwang, Y. Y. A self-organization algorithm for real-time flood forecast. Hydrol. Process. 13, 123–138 (1999).
Google Scholar
Dawson, C. W. & Wilby, R. L. A comparison of artificial neural networks used for river flow forecasting. Hydrol. Earth Syst. Sci. 3, 529–540 (1999).
Google Scholar
Cigizoglu, H. K. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv. Water Res. 27, 185–195 (2004).
Google Scholar
Dransfeld, S., Tatnall, A. R., Robinson, I. S. & Mobley, C. D. A comparison of multi-layer perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra. Int. J. Remote Sens. 25, 4829–4834 (2004).
Google Scholar
Pankiewicz, G. S. Neural network classification of convective airmasses for a flood forecasting system. Int. J. Remote Sens. 18, 887–898 (1997).
Google Scholar
Addor, N., Newman, A. J., Mizukami, N. & Clark, M. P. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrol. Earth Syst. Sci. 21, 5293–5313 (2017).
Google Scholar
Newman, A. et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci. 19, 209 (2015).
Google Scholar
Kratzert, F. et al. Caravan—a global community dataset for large-sample hydrology. Sci. Data https://doi.org/10.1038/s41597-023-01975-w (2023).
GEMStat Database of the Global Environment Monitoring System for Freshwater (GEMS/Water) Programme (UN Environment Programme, 2018).
Hartmann, J., Lauerwald, R. & Moosdorf, N. GLORICH—global river chemistry database. Pangaea 902360, 520 (2019).
Rotteveel, L., Heubach, F. & Sterling, S. M. The Surface Water Chemistry (SWatCh) database: a standardized global database of water chemistry to facilitate large-sample hydrological research. Earth Syst. Sci. Data 14, 4667–4680 (2022).
Google Scholar
Sterle, G. et al. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data. Hydrol. Earth Syst. Sci. Discuss. 2022, 1–23 (2022).
D’Alimonte, D. & Zibordi, G. Phytoplankton determination in an optically complex coastal region using a multilayer perceptron neural network. IEEE Trans. Geosci. Remote Sens. 41, 2861–2868 (2003).
Google Scholar
