Flood prevention and disaster mitigation have a great impact on people's lives and properties, and so it is urgent to realise high-accuracy inflow predictions for flood early warning. To this end, a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir. Firstly, a data formatting scheme called the 'hydrological regime profile' is designed for input data. The whole data set is partitioned into a low-flow subset and a high-flow subset. Considering the high dimensions of the complex input data, convolutional neural networks (CNN), EXtreme gradient Boosting model (XGBoost) and a partial least squares model (PLS) are used. In the CNN and XGBoost models, a special loss function weighted on inflow is designed to improve the performance on high-inflow predictions. Finally, a multi-model combination method is proposed to improve the prediction performance. Compared with XGBoost, CNN and PLS, the root mean square error of the combined model is reduced by 41.64%, 72.29% and 3.41%, respectively. As a consequence, the combined model is able to predict the inflows with higher accuracy compared to the single models.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
In this paper a scheme increasing raster images stored in a WORM is studied. A twodimensional coding method improved from the CCiTT ( Comite Consultatif Interationale Telegraphie et Telephonie) is used to compress the image data. And a means of saving image to the WORM in batches is taken to increase the WORM space utilization ratio.
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