Ensemble learning of daily river discharge modeling for two watersheds with different climates

Climate change has increased the importance of rainfall forecasting, because it has changed the duration and frequency of floods and increased their magnitude Ashrafi, Khoie, et al., 2022). As a result of inundation, landslides, and debris flows in urban areas, heavy rainfall often leads to fatalities and significant financial damages (Seo et al., 2014). Furthermore, overflow resulting from heavy rainfall is a primary contributor to the transport of water pollutants (Lintern et al., 2018). Various preventive measures have been developed, including flood warning systems, to alleviate damages caused by rainfall and enable urban flood management infrastructures to operate more efficiently. Therefore, accurate and reliable rainfall forecasting is essential in urban areas (Poornima & Pushpalatha, 2019). Reliable rainfall nowcasting is a challenging and complex undertaking since rainfall features are influenced by various variables, such as pressure, temperature, relative humidity, and wind speed (Govindaraju, 2000). Due to the difficulty in obtaining meteorological data and the complexity of estimating relationships among variables, physical models are not usually suitable for rainfall nowcasting. An effective alternative is to use empirical models to determine the relationship between inputs like temperature and relative humidity and outputs like rainfall (Nasseri et al., 2008). To ensure optimal performance for urban drainage infrastructures, rainfall nowcasting frameworks should be able to make accurate forecasts with different lead times. Researches on rainfall forecasting and its influencing factors have been conducted for decades, and many approaches have been proposed. There are various traditional forecasting models in the literature, which have been developed using classic time series algorithms such as linear regression, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural networks (ANNs), numerical weather predictions (NWPs), and Fuzzy logic algorithm (

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“…Detailed information regarding the error function calculation may be found in Gupta (2015) and Xu et al. (2020).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the Python Scikit-Learn package is used to implement the Bagging, Random Forest, and Adaboost ensemble techniques. Detailed information regarding the error function calculation may be found in Gupta (2015) and Xu et al (2020).…”

Section: Adaboostmentioning

confidence: 99%

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Amini

Dolatshahi

Kerachian

2023

Water Resources Research

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“…Previously, researchers employed techniques processing the statistics accumulated using various methods (regression, approximative, and others [27][28][29][30][31]). Widespread adoption of neural networks and machine learning contributed to their use in many works to predict runoff (for example, [32][33][34][35][36][37][38][39][40]). This paper presents one of the approaches to calculating predictive estimates of effective water inflow into a lake and flow rates of rivers with the aid of the GeoGIPSAR system and neural network.…”

Section: Initial Data Organization Of Monitoring Of the Atmospheric State Datamentioning

confidence: 99%

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

et al. 2021

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The paper is concerned with a methodological approach to monitoring the state of atmospheric parameters in the catchment area of Lake Baikal, including real-time analysis of actual distributed data with the determination of analog years according to the preset proximity of comparative indicators and the most probable long-term predictive distributions of surface temperatures, precipitation, pressure, and geopotential with a lead time of up to 9–12 months. We have developed the information-analytical system GeoGIPSAR to conduct the real-time analysis of spatial and point data by various processing methods and obtain long-term prognostic estimates of water inflow into the lake.

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“…Moreover, the uncertainty caused by the distinctive theory and structure of a given model's algorithms, may render its successful application regionspeci c (Zhang et al, 2015). Implementing an ensemble learning strategy can prove to be an effective approach to reducing forecasting uncertainty (Demargne et al, 2014;Xu et al, 2020;Ossandón et al, 2021). By integrating the diverse forecasts of different machine learning models, the statistical, computational, and representation problems of employing a single model can be addressed (Zounemat-Kermani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

Yang

Feng

et al. 2023

Preprint

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As much as accurate streamflow forecasts are important and significant for arid regions, they remain deficient and challenging. An ensemble learning strategy of decomposition-based machine learning and deep learning models was proposed to forecast multi-time-step ahead streamflow for northwest China’s Dunhuang Oasis. The efficiency and reliability of a Bayesian Model Averaging (BMA) ensemble strategy for 1-, 2-, and 3-day ahead streamflow forecasting was evaluated in comparison with decomposition-based machine learning and deep learning models: (i), a variational-mode-decomposition model coupled with a deep-belief-network model (VMD-DBN), (ii) a variational-mode-decomposition model coupled with a gradient-boosted-regression-tree model (VMD-GBRT), (iii) a complete ensemble empirical mode decomposition with adaptive noise model coupled with a deep belief network model (CEEMDAN-DBN), and (iv) a complete ensemble empirical mode decomposition with adaptive noise model with a gradient boosted regression tree coupled model (CEEMDAN-GBRT). Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe coefficient (NSE) values of 0.976, 0.967, and 0.957, the BMA model achieved the greatest accuracy for 1-, 2-, and 3-day ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of the BMA model in yielding consistently accurate streamflow forecasts. Thus, the BMA ensemble strategy could provide an efficient alternative approach to multi-time-step ahead streamflow forecasting for areas where physically-based models cannot be used due to a lack of land surface data. The application of the BMA model was particularly valuable when the ensemble members gave equivalent satisfactory performances, making it difficult to choose amongst them.

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Ensemble learning of daily river discharge modeling for two watersheds with different climates

Cited by 9 publications

References 31 publications

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

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