Abstract
The region of Tehri is vulnerable to heavy rainfall and floods and other natural disasters. There is a need for an efficient flood forecasting system in the state of Uttarakhand so that preventive measures can be taken before the event occurs in any area. Artificial Neural Networks are a great tool to analyse complex systems in terms of simple weights and biases. The ANN once used efficiently to train, validate and test different datasets on a large scale can be an effective tool for flood forecast. In this paper, we have used monthly data of rainfall and discharge from the year 1964 to 2012 to train and test an ANN model with three hidden layers. Later the climate change data is used to estimate the rainfall for the future century and that rainfall is used as an input for the trained model to estimate the flood for the coming century (up to 2099). The results have been proven to be satisfactory in the training stage and more than 10 instances of high floods are forecasted for the future using climate change inputs.
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Rajeev Gandhi, B.G., Kumar, D., Yadav, H.L. (2020). An Artificial Neural Network Model for Estimating the Flood in Tehri Region of Uttarakhand Using Rainfall Data. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_43
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DOI: https://doi.org/10.1007/978-981-15-4032-5_43
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