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Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network

Reddy Beeram Satya Narayana, Pramada S. K., Roshni Thendiyath

Acta Geophysica

2022

Vol. 70, no 2

847--857

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.

Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches

Sithara S., Pramada S. K., Thampi Santosh G.

Acta Geophysica

2020

Vol. 68, no. 6

1779--1790

Climate change is expected to adversely afect the coastal ecosystem in many ways. One of the major consequences of climate change in coastal areas is sea level rise. In order to manage this problem efciently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identifcation of climatic variables infuencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the infuencing variables, and support vector machine (SVM) and hybrid wavelet support vector machine (WSVM) techniques were used for sea level prediction. Sea surface temperature, sea surface salinity, and mean sea level pressure were observed to be the major local climatic variables infuencing sea level. Halosteric efect is found to have a major impact on the sea level. The variables identifed were subsequently used as predictors in both SVM and WSVM. WSVM employs discrete wavelet transform to decompose the variables before being input to the SVM model. The performance of both the models was compared using statistical measures such as root mean square error (RMSE), correlation coefcient (r), coefcient of determination (r 2 ), average squared error, Nash–Sutclife efciency, and percentage bias along with graphical indicators such as Taylor diagrams and regression error characteristic curves. Results indicate that the WSVM model predicted sea level with an RMSE of 0.029 m during the training and 0.040 m during the testing phases. The corresponding values for SVM are 0.043 m and 0.069 m, respectively. Also, the other statistical measures and graphical indicators suggest that WSVM technique outperforms the SVM approach in the prediction of sea level.