Abstract
Rainfall–runoff is an extremely complex procedure because of nonlinear and multidimensional dynamics, and therefore not easy for modeling. In recent years, artificial intelligence methods are utilized to model hydrogeological time series. Present study considers nonlinear multiple regressions (NMR), feedforward backpropagation neural network (FFBPN), and adaptive neuro-fuzzy inference system (ANFIS) to predict runoff of Thiruvananthapuram watershed, Kerala. Infiltration and evapotranspiration loss, precipitation, and average temperature are taken into input scenario while runoff acts as output for model. The estimation results obtained by using neuro-fuzzy technique are tested and contrasted to those of artificial neural networks (ANNs). Root mean squared errors (RMSE) and coefficient of determination (R2) are utilized as assessing criterion to evaluate the model performances. Based on research finding ANFIS provides superlative value for RMSE and R2 0.9625 and 0.9814, but for NMR and FFBPNN it delivers 0.9376 and 0.9592, respectively. Assessment outcomes show that ANFIS is better suited to apply for runoff estimation.
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Jimmy, S.R., Sahoo, A., Samantaray, S., Ghose, D.K. (2021). Prophecy of Runoff in a River Basin Using Various Neural Networks. In: Satapathy, S.C., Bhateja, V., Ramakrishna Murty, M., Gia Nhu, N., Jayasri Kotti (eds) Communication Software and Networks. Lecture Notes in Networks and Systems, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-15-5397-4_72
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DOI: https://doi.org/10.1007/978-981-15-5397-4_72
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