Quantum Neural Networks Approach for Water Discharge Forecast
Source abstract
Abstract
Predicting the river discharge is essential for preparing effective measures against flood hazards or managing hydrological droughts. Despite mathematical modeling advancements, most algorithms have failed to capture the extreme values (especially the highest ones). In this article, we proposed a quantum neural networks (QNNs) approach for forecasting the river discharge in three scenarios. The algorithm was applied to the raw data series and the series without aberrant values. Comparisons with the results obtained on the same series by other neural networks (LSTM, BPNN, ELM, CNN-LSTM, SSA-BP, and PSO-ELM) emphasized the best performance of the present approach. The lower error between the recorded values and the predicted ones in the evaluation of maxima compared to the case of the competitors mentioned shows that the algorithm best fits the extremes. The most significant mean standard errors (MSEs) and mean absolute errors (MAEs) were 26.9424 and 4.8914, respectively, and the lowest R2 was 84.36%, indicating the good performances of the algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Applications · Energy Load and Power Forecasting · Hydrological Forecasting Using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Key Laboratory of Deep Oil and Gas and School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Department of Civil Engineering, Transilvania University of Brașov, 5, Turnului Street, 500152 Brașov, Romania
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