Abstract
Knowledge about water table depth fluctuation is vastly necessary for appropriate water resources planning and sustainable development. In this paper, wavelet based artificial neural network (W-ANN) is developed for water table depth (WTD) forecasting. Relative performance of proposed W-ANN was compared with regular ANN models considering different performance indicators. The WI and NSE values for W-ANN model were 0.9924 and 0.9901 and for ANN model was 0.9538 and 0.9519 respectively. It was revealed that application of wavelet analysis can increase forecasting accuracy of conventional ANN with more model accuracy and reliability. Level of disintegration in wavelet analysis should be found based on seasonality and periodicity of data series.
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Patel, N., Bhoi, A.K., Paika, D.K., Sahoo, A., Mohanta, N.R., Samantaray, S. (2022). Water Table Depth Forecasting Based on Hybrid Wavelet Neural Network Model. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_22
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