Forecasting surface water temperature in lakes: A comparison of approaches

Zhu, Senlin; Ptak, Mariusz; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Dai, Jiangyu; Sivakumar, Bellie

doi:10.1016/j.jhydrol.2020.124809

Cited by 74 publications

(40 citation statements)

References 53 publications

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“…The Global Runoff Data Center (GRDC, available at http://grdc.bafg.de/), for example, shows that a large portion of basins around the world have fewer than 3 years’ worth of daily streamflow observations. In these scenarios, machine learning models have still been employed but mostly in a local model setting, where a model is fitted to the data from one basin or a few neighboring basins (S. Zhu et al., 2020; Yaseen et al., 2015; Liang et al., 2018; Bowes et al., 2019; de la Fuente et al., 2019). Shen (2018) provided a summary and an entry point into a vast body of work in this realm, with many other papers also attesting to the huge demand for solutions (Beven, 2020; Guillon et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

Feng

Lawson

et al. 2021

Water Resources Research

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There is a great deal of geographic imbalance in global hydrologic data sets. Outside of the US and parts of Europe, there are many parts of the world that have only sparsely available streamflow gauge networks with only a few years' worth of data (Do et al., 2017;Fekete & Vörösmarty, 2007). Besides streamflow gauges, these regions also lack data on physiographic attributes such as geology and soil depth. Nevertheless, climate change is stressing these parts of the world, and accurate hydrologic simulations are needed for these regions just as much, or even more than for data-rich regions.Catchments across the world are often perceived as being unique from each other, requiring customized model development for each basin (Teutschbein & Seibert, 2012). As a rule of thumb, when we create process-based hydrologic models, our development effort scales roughly linearly to the modeled area, computational effort scales linearly at best, and accuracy is unrelated to the number of basins modeled. It is typically difficult to apply knowledge gained from one basin to another, as parameters or experiences do not transfer easily. As a result, although there have been calls for hydrologic studies to transcend the uniqueness

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Section: Introductionmentioning

confidence: 99%

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

Feng

Lawson

et al. 2021

Water Resources Research

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“…Hence, empirical formulation is demonstrated as a remarkable limitation on the ET0 estimation. During the last few decades, models based on computer aid capacity indicated a distinguished progress in the hydrology and water resources fields [9][10][11][12][13]. Artificial intelligence (AI) models have been extensively applied as a reliable soft computing technology for ET0 estimation based on the available and measured climatic variables [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

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“…With the growing research interests in artificial intelligence and the concept of big data, more data-driven methods, especially machine learning and deep learning methods, have been developed [29]. These methods have been widely used in a large number of areas such as economics [30], geoscience [23,31,32], and medicine [33]. Riazi [23] also successfully applied the deep learning approach to accurately predict the coastal tide level, which clearly indicated the applicability of machine learning methods in analyzing the estuarine tides.…”

Section: Introductionmentioning

confidence: 99%

Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River

Gan

Pan

Chen

et al. 2021

JMSE

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Due to the strong nonlinear interaction with river discharge, tides in estuaries are characterised as nonstationary and their mechanisms are yet to be fully understood. It remains highly challenging to accurately predict estuarine water levels. Machine learning methods, which offer a unique ability to simulate the unknown relationships between variables, have been increasingly used in a large number of research areas. This study applies the LightGBM model to predicting the water levels along the lower reach of the Columbia River. The model inputs consist of the discharges from two upstream rivers (Columbia and Willamette Rivers) and the tide characteristics, including the tide range at the estuary mouth (Astoria) and tide constituents. The model is optimized with the selected parameters. The results show that the LightGBM model can achieve high prediction accuracy, with the root-mean-square-error values of water level being reduced to 0.14 m and the correlation coefficient and skill score being in the ranges of 0.975–0.987 and 0.941–0.972, respectively, which are statistically better than those obtained from physics-based models such as the nonstationary tidal harmonic analysis model (NS_TIDE). The importance of subtide constituents in interacting with the river discharge in the estuary is clearly revealed from the model results.

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Forecasting surface water temperature in lakes: A comparison of approaches

Cited by 74 publications

References 53 publications

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River

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