Volume-11 , Issue-10 , Oct - 2023 Go Back

This paper focuses on hydrological drought forecasting, using Artificial Neural Network (ANN) and predicts the values of hydrological drought condition derived using Narmada River Water level data of Hoshangabad (M.P). We have used the water level data as input data of ANN model for hydrological drought forecasting, and determine Standardized Water Level Index (SWLI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

Artificial Neural Network, Hydrological Drought, RWL.

[1] Agnew, C. T.: Using the SPI to identify drought. Drought Network News, Vol.12, Issue.1, pp.6–11, 1999.
[2] Bankert, R. L.: Cloud classification of AVHRR Imagery in maritime regions using a probabilistic neural network, J. Appl. Meteorol., Vol.33, pp.909–918, 1994.
[3] Marzban, C. and Stumpf, G. J.: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteor., Vol.35, pp.617–626, 1996.
[4] Mu¨ller, B., and Reinhardt, J.: Neural Networks: An Introduction, the Physics of Neural Networks Series, Springer-Verlag, Vol.2, pp 266, 1991.

Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma, "Hydrological Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Hydrological Drought Condition Derived using River Water Level Data," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.71-74, 2023.