Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm

Zhou, Jincheng; Wang, Dan; Band, Shahab S.; Jun, Changhyun; Bateni, Sayed M.; Moslehpour, Massoud; Pai, Hao-Ting; Hsu, Chung-Chian; Ameri, Rasoul

doi:10.1007/s11269-023-03534-9

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…They do not confirm the similar forecasting power of ANN and ARMA found by Abrahart and See [18] in a case study from Turkey. Our study output is in concordance with the findings from [17,76,77] that emphasized the better performances of the neural networks against ARIMA, given the better ability to learn of the neural network and less sensitivity to the abrupt changes in the time series.…”

Section: Discussionsupporting

confidence: 90%

“…Therefore, other approaches have been proposed to address these drawbacks. Some of them are artificial intelligence (AI), or machine learning (ML), models that do not consider mathematical relationships, utilizing only sets of input parameters, in contrast with the physical models that utilize mathematical tools to predict hydrological phenomena [17].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

Zhen,

Bărbulescu

2024

Water

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Modeling and forecasting the river flow is essential for the management of water resources. In this study, we conduct a comprehensive comparative analysis of different models built for the monthly water discharge of the Buzău River (Romania), measured in the upper part of the river’s basin from January 1955 to December 2010. They employ convolutional neural networks (CNNs) coupled with long short-term memory (LSTM) networks, named CNN-LSTM, sparrow search algorithm with backpropagation neural networks (SSA-BP), and particle swarm optimization with extreme learning machines (PSO-ELM). These models are evaluated based on various criteria, including computational efficiency, predictive accuracy, and adaptability to different training sets. The models obtained applying CNN-LSTM stand out as top performers, demonstrating a superior computational efficiency and a high predictive accuracy, especially when built with the training set containing the data series from January 1984 (putting the Siriu Dam in operation) to September 2006 (Model type S2). This research provides valuable guidance for selecting and assessing river flow prediction models, offering practical insights for the scientific community and real-world applications. The findings suggest that Model type S2 is the preferred choice for the discharge forecast predictions due to its high computational speed and accuracy. Model type S (considering the training set recorded from January 1955 to September 2006) is recommended as a secondary option. Model type S1 (with the training period January 1955–December 1983) is suitable when the other models are unavailable. This study advances the field of water discharge prediction by presenting a precise comparative analysis of these models and their respective strengths

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

Zhen,

Bărbulescu

2024

Water

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“…Streamflow, being the primary accessible water resource for human beings, necessitates the implementation of appropriate engineering and non-engineering measures, such as reservoir operation and water resource dispatching, as effective strategies for optimizing their utilization. Reliable short-term streamflow forecasting stands as a crucial assurance for the smooth implementation of these methods [2]. Conversely, flooding remains one of the major natural meteorological disasters worldwide, characterized by sudden onset, extensive reach, and severity.…”

Section: Introductionmentioning

confidence: 99%

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Lin,

Wang,

Jiang

et al. 2024

Water

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Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE > 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models.

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Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique

Yang,

Chen,

Qin

2023

Water Resour Manage

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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm

Cited by 7 publications

References 55 publications

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique

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