Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit, Pakorn; Pinthong, Sirimon; Salaeh, Nureehan; Binnui, Fadilah; Khwanchum, Laksanara; Pham, Quoc Bao

doi:10.1038/s41598-021-99164-5

Cited by 20 publications

(9 citation statements)

References 44 publications

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“…While the current study focused on the RR modeling concept by considering all inputs at the t (same) time and by conserving several meteorological variables as input of black-box models to have interpretation meaning for RR modeling. Previous researches confirm the result of the current study, for example, Ditthakit et al 13 used the black-box model to increase the efficiency of the white-box model in Thailand. This means that the method presented in this study can be expanded by other models and also can generalize the implementation of this method in other regions with different climates.…”

Section: Discussionsupporting

confidence: 88%

“…In addition, various meteorological and climatological variables like temperature, evaporation, humidity, and air pressure provoke the volume of runoff 9 . The nature of hydrological systems could be monitored through the various types of hydrological models which give a deeper insight into the physical interaction between the various parameters and their response to each other 10 – 13 .…”

Section: Introductionmentioning

confidence: 99%

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IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling

Mohammadi

Safari

Vazifehkhah

2022

Sci Rep

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As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff studies, water supply, irrigation issues, and environmental management. Among the variety of approaches for RR modeling, conceptual approaches use physical concepts and are appropriate methods for representation of the physics of the problem while may fail in competition with their advanced alternatives. Contrarily, machine learning approaches for RR modeling provide high computation ability however, they are based on the data characteristics and the physics of the problem cannot be completely understood. For the sake of overcoming the aforementioned deficiencies, this study coupled conceptual and machine learning approaches to establish a robust and more reliable RR model. To this end, three hydrological process-based models namely: IHACRES, GR4J, and MISD are applied for runoff simulating in a snow-covered basin in Switzerland and then, conceptual models’ outcomes together with more hydro-meteorological variables were incorporated into the model structure to construct multilayer perceptron (MLP) and support vector machine (SVM) models. At the final stage of the modeling procedure, the data fusion machine learning approach was implemented through using the outcomes of MLP and SVM models to develop two evolutionary models of fusion MLP and hybrid MLP-whale optimization algorithm (MLP-WOA). As a result of conceptual models, the IHACRES-based model better simulated the RR process in comparison to the GR4J, and MISD models. The effect of incorporating meteorological variables into the coupled hydrological process-based and machine learning models was also investigated where precipitation, wind speed, relative humidity, temperature and snow depth were added separately to each hydrological model. It is found that incorporating meteorological variables into the hydrological models increased the accuracy of the models in runoff simulation. Three different learning phases were successfully applied in the current study for improving runoff peak simulation accuracy. This study proved that phase one (only hydrological model) has a big error while phase three (coupling hydrological model by machine learning model) gave a minimum error in runoff estimation in a snow-covered catchment. The IHACRES-based MLP-WOA model with RMSE of 8.49 m3/s improved the performance of the ordinary IHACRES model by a factor of almost 27%. It can be considered as a satisfactory achievement in this study for runoff estimation through applying coupled conceptual-ML hydrological models. Recommended methodology in this study for RR modeling may motivate its application in alternative hydrological problems.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling

Mohammadi

Safari

Vazifehkhah

2022

Sci Rep

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“…The GR2M is a conceptual monthly hydrological model developed by Makhlouf in 1990s [37] which has been continuously updated by many researchers and employed in many areas across the world [38][39][40][41][42][43][44]. The structures and equations of the GR2M model are shown in Figure 2.…”

Section: Hydrological Modelmentioning

confidence: 99%

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Guo

Wang

et al. 2022

Remote Sensing

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Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied.

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“…Multiple time-series issues require prediction of a sequence of future values using only observed historical data [10][11][12][13][14][15]. Multistep-ahead prediction describes the process of attempting to forecast potential events in a time series [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have applied SVR, RF, and ANN in the literature. For example, Yang et al [12] found a difference in the performance of SVR, RF, and the artificial neural network (ANN) for predicting one-month-ahead reservoir water inflows in China and the USA. The results show the RF technique has the highest performance with important climate indices such as NINO1, NINO3, NINO4, etc.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow

Weekaew

Ditthakit

Pham

et al. 2022

Water

Self Cite

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Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy of reservoir inflow forecasting is one of the essential factors supporting reservoir operations. This study aimed to investigate coupling models of feature selection (FS) and machine learning (ML) algorithms to predict the monthly reservoir inflow. The study was carried out using data from the Huai Nam Sai reservoir in southern Thailand. Eighteen years of monthly recorded data (i.e., reservoir inflow, reservoir storage, rainfall, and regional climate indices) with up to a 12-month time lag were utilized. Three ML techniques, i.e., multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN)were compared in their capabilities. In addition, two FS techniques, i.e., genetic algorithm (GA) and backward elimination (BE) methods, were studied with four predictable time intervals, consisting of 3, 6, 9, and 12 months in advance. Ten-fold cross-validation was used for model evaluation. Study results revealed that FS methods (i.e., GA and BE) Could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. BE-ANN provided the best performance for three-time-ahead (T + 3) and nine-time-ahead (T + 9) by giving an OI of 0.9885 and 0.8818, NSE of 0.9546 and 0.9815, RMSE of 1.3155 and 1.2172 MCM/month, MAE of 0.9568 and 0.9644 MCM/month, and r of 0.9796 and 0.9804, respectively. The GA-ANN model showed the highest prediction accuracy for six-time-ahead (T + 6), with an OI of 0.8997, NSE of 0.9407, RMSE of 2.1699 MCM/month, MAE of 1.7549 MCM/month, and r of 0.9759. The ANN model showed the best prediction accuracy for twelve-time-ahead (T + 12), with an OI of 0.9515, NSE of 0.9835, RMSE of 1.1613 MCM/month, MAE of 0.9273 MCM/month, and r of 0.9835.

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Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Cited by 20 publications

References 44 publications

IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling

IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow

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