Abstract
The advancements in artificial intelligence play a significant role in solving the problems of researchers and engineers to develop prediction models with higher accuracy over the analytical and numerical models. The wavelet ensemble artificial intelligence model has a widespread application in forecasting hydrological datasets. The signal decomposition type, level and the mother wavelet affect the model performance in wavelet-based approaches. The present analysis focuses on studying the significance of the level and type of decomposition in wavelet transform for pre-processing the input variables to predict the target variable. In this work, to forecast seasonal suspended sediment load of the Kallada River basin in Kerala, two types of decomposition with decomposition levels ranging from 2 to 7 were adopted using seasonal flow data (wet and dry seasons). To rank the WANN models, compromise programming was adopted using the results based on statistical performance indicators and compared with the performance of the conventional FFNN model. From the accuracy assessment and ranking, type-2 with 5th level decomposition can capture the actual periodicity of the signal and predict the suspended sediment load with higher accuracy. It also shows the capability to predict the extreme events of time series.
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The authors would like to acknowledge the anonymous reviewers, associate editor and editor for their insightful comments and suggestions.
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BSNReddy: Formal assessment, conceptualization, data collection, framework of methodology, model development, software, initial draft writing, review and editing. SKP: Formal assessment, conceptualization, methodology, resources, supervision and validation. TR: Formal assessment, conceptualization, methodology, resources, supervision and validation.
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Edited by Dr. Robert Bialik (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).
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Reddy, B.S.N., Pramada, S.K. & Roshni, T. Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network. Acta Geophys. 70, 847–857 (2022). https://doi.org/10.1007/s11600-022-00761-3
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DOI: https://doi.org/10.1007/s11600-022-00761-3