Title
Hydrological Predictions: Using DataDriven Models Coupled with Data Preprocessing Techniques,Used
Delivery time: 8-12 business days (International)
This book makes an endeavor to improve the accuracy of hydrological forecasting in three aspects, model inputs, selection of models, and datapreprocessing techniques. Seven input techniques, namely, linear correlation analysis (LCA), false nearest neighbors, correlation integral, stepwise linear regression, average mutual information, partial mutual information, artificial neural network (ANN) based on multiobjective genetic algorithm, are first examined to select optimal model inputs in each prediction scenario. Representative models, such as Knearestneighbors (KNN) model, dynamic system based model (DSBM), ANN, modular ANN (MANN), and hybrid artificial neural networksupport vector regression (ANNSVR), are then proposed to conduct rainfall and streamflow forecasts. Four datapreprocessing methods including moving average (MA), principal component analysis (PCA), singular spectrum analysis (SSA), and wavelet analysis (WA), are further investigated by integration with the abovementioned forecasting models.
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We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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