Academic Journal
Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation
| Τίτλος: | Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation |
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| Συγγραφείς: | Chenmin Ni, Muhammad Fadhil Marsani, Fam Pei Shan, Xiaopeng Zou |
| Πηγή: | AIMS Mathematics, Vol 9, Iss 6, Pp 14681-14696 (2024) |
| Στοιχεία εκδότη: | American Institute of Mathematical Sciences (AIMS), 2024. |
| Έτος έκδοσης: | 2024 |
| Θεματικοί όροι: | Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, 0211 other engineering and technologies, Convolutional neural network, 02 engineering and technology, flood prediction, Systems engineering, Global Flood Risk Assessment and Management, Engineering, Hydrological Modeling using Machine Learning Methods, Machine learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, temporal convolutional networks, Water Science and Technology, Global and Planetary Change, Geography, 4. Education, FOS: Environmental engineering, Unit (ring theory), gated recurrent units, Groundwater Level Forecasting, Flood myth, 15. Life on land, Watershed Simulation, Computer science, Mathematics education, 6. Clean water, Hydrological Modeling and Water Resource Management, subtraction-average-based optimizer, Archaeology, 13. Climate action, Environmental Science, Physical Sciences, kernel density estimation, Estimation, Mean squared prediction error, Flood Inundation Modeling, Mathematics, Forecasting |
| Περιγραφή: | Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing a hybrid deep learning model for both point and interval flood prediction is presented, enhanced by improved kernel density estimation (KDE) for prediction comparison and error simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) is constructed by tuning the internal structure of the TCN, the activation function, the L2 regularization, and the optimizer. Then, Pearson Correlation is used for feature selection, and the hyperparameters of the improved GRU-TCN are optimized by the subtraction-average-based optimizer (SABO). To further assess the prediction uncertainty, interval predictions are provided via Non-parametric KDE, with an optimized bandwidth setting for accurate error distribution simulation. Experimental comparisons are made on 5-year hydro-meteorological daily data from two stations along the Yangtze River. The proposed model surpasses long short-term memory network (LSTM), TCN, GRU, TCN-LSTM, and GRU-TCN, with a reduction of more than 13% in root mean square error (RMSE) and approximately 15% in mean absolute error (MAE), resulting in better interval estimation and error control. The improved kernel density estimation curves for the errors are closer to the mean value of the confidence intervals, better reflecting the trend of the error distribution. This research enhances the accuracy and reliability of flood predictions and improves the capacity of humans to cope with climate and environmental changes. |
| Τύπος εγγράφου: | Article Other literature type |
| ISSN: | 2473-6988 |
| DOI: | 10.3934/math.2024714 |
| DOI: | 10.60692/087gk-0h297 |
| DOI: | 10.60692/xd0e9-w5r55 |
| Σύνδεσμος πρόσβασης: | https://doaj.org/article/f8eff84a931d40fbb0883ce291ca6f1f |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....58d66c61ae1f554f9f2ce164cbe52ca2 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 24736988 |
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| DOI: | 10.3934/math.2024714 |