![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Prediction of Annual Maximum Rainfall Using Artificial Neural Network Approach
Prediction of rainfall on a given time period (daily, monthly, seasonal and annual) is of utmost importance for planning of irrigation and drainage system as also for command area development. With the development of Artificial Intelligence (AI), number of AI methods such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System, Fuzzy Logic, Support Vector Machine and Evolutionary Optimization Algorithm are generally applied for rainfall prediction. Out of which, ANN has an ability to obtain complicated non-linear relationship between the variables, which is suitable to predict the rainfall. This paper presented a study on prediction of annual maximum rainfall (AMR) of Gaganbawada, Lanja and Radhanagari using Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks of ANN. For this purpose, the annual maximum series of meteorological data viz., rainfall, minimum and maximum temperature and average wind speed was generated from the daily data observed at Gaganbawada (1950 to 2020), Lanja (1950 to 2021) and Radhanagari (1950 to 2021), and used as an input for prediction of AMR through MLP and RBF. The performance of the MLP and RBF networks applied in rainfall prediction was evaluated by model performance indicators such as correlation coefficient, Nash-Sutcliffe model efficiency and root mean squared error. The study showed that MLP is better suited amongst two networks of ANN applied for prediction of AMR of Gaganbawada, Lanja and Radhanagari.
Keywords
Correlation Coefficient, Multi-Layer Perceptron, Radial Basis Function, Model Efficiency, Rainfall, Mean Squared Error.
User
Font Size
Information
- S.K. Nanda, D.P. Tripathy, S.K. Nayak and S. Mohapatra, Prediction of rainfall in India using artificial neural network models, IJISA International Journal of Intelligent Systems and Applications, 5 (12), 2013, 1-22.
- A.H. El-Shafie, A. El-Shafie, H.G. El Mazoghi, A. Shehata and M.R. Taha, Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt, International Journal of the Physical Sciences, 6(6), 2011, 1306-1316.
- A. Al Mamun, M.N. Bin Salleh and H.M. Noor, Estimation of short-duration rainfall intensity from daily rainfall values in Klang valley, Malaysia, Applied Water Science, 8(7), 2018, 1-10.
- B.K. Raghavendra and Jay B. Simha, Evaluation of feature selection methods for predictive modeling using neural networks in credits scoring, International Journal of Advanced Networking and Applications, 2(3), 2010, 714-718.
- P.C. Nayak, B. Venkatesh, B. Krishna and S.K. Jain, Rainfall-runoff modelling using conceptual, data driven, and wavelet based computing approach, Journal of Hydrology, 493(1), 2013, 57-67.
- N. Vivekanandan, Prediction of rainfall using MLP and RBF networks, International Journal of Advanced Networking and Applications, 5(4), 2014, 1974-1979.
- E.G. Dada, H.J. Yakubu and D.O. Oyewola, Artificial neural network models for rainfall prediction, European Journal of Electrical Engineering and Computer Science, 5(2), 2021, 30-35.
- J.T. Esteves, G. De Souza Rolim and A.S. Ferraudo, Rainfall prediction methodology with binary multilayer perceptron neural networks, Climate Dynamics, 52(3-4), 2019, 2319–2331.
- S. Poornima and M. Pushpalatha, Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units, Atmosphere, 10(11), 2019, Article ID.668.
- A.R. Senthil Kumar, K.P. Sudheer, S.K. Jain and P.K. Agarwal, Rainfall-runoff modelling using artificial neural networks: comparison of network types, Hydrological Processes, 19(6), 2004, 1277- 1291.
- J. Wu, Prediction of rainfall time series using modular RBF neural network model coupled with SSA and PLS, Proceedings of ACIIDS 2012, Part II, LNAI 7197, 509-518.
- D.R. Nayak, A. Mahapatra and P. Mishra, A survey on rainfall prediction using artificial neural network, International Journal of Computer Applications, 72(16), 2013, 32-40.
- B. Choubin, A. Malekian and M. Golshan, Application of several data-driven techniques to predict a standardized precipitation index, Atmósfera, 29(2), 2016, 121-128.
- I.M. Sofian, A.K. Affandi, I. Iskandar and Y. Apriani, Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function, International Journal of Advances in Intelligent Informatics, 4(2), 2018, 154-166.
- Y. Dash, S.K. Mishra and B.K. Panigrahi, Rainfall prediction for the Kerala state of India using artificial intelligence approaches, Computers & Electrical Engineering, 70(1), 2018, 66-73.
- S.S. Chai, W.K. Wong, K.L. Goh, H.H. Wang and Y.C. Wang, Radial Basis Function (RBF) neural network: Effect of hidden neuron number, training data size, and input variables on rainfall intensity forecasting, International Journal on Advanced Science Engineering Information Technology, 9(6), 2019, 1921-1926.
- Q. Liu, Y. Zou, X. Liu and N. Linge, A survey on rainfall forecasting using artificial neural network, International Journal of Embedded Systems, 11(2), 2019, 240-249.
- L.C.P. Velasco, R.P. Serquiña, M.A. Abdul Zamad, B.F. Juanico and J.C. Lomocso, Week-ahead rainfall forecasting using multilayer perceptron neural network, Procedia Computer Science, 161, 2019, 386-397.
- D. Endalie, G. Hailea and W. Taye, Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia, Water Supply, 22(3), 2021, 3448.
- K. Dutta and P. Gouthaman, Rainfall prediction using machine learning and neural network, International Journal of Recent Technology and Engineering, 9(1), 2020, 954-1961.
- S.D. Nandakumar, R. Valarmathi, P. Sudha Juliet and G. Brindha, Artificial neural network for rainfall analysis using deep learning techniques, Proceedings of ICACSE 2020 published in Journal of Physics: Conference Series 042022, 2021.
- X. Zhang, S.N. Mohanty, A.K. Parida, S.K. Pani, B. Dong and X. Cheng, Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha, IEEE Access, 8, 2020, 30223-30233.
- S. Jalal, K. Özgur, M. Oleg, S. Abbas-Ali and N. Bagher, Forecasting daily stream flows using artificial intelligence approaches, ISH Journal of Hydraulic Engineering, 18(3), 2012, 204-214.
- H. E. Amr, A. El-Shafie, G. E. Hasan, A. Shehata and M. R. Taha, Artificial neural network technique for rainfall forecasting applied to Alexandria, International Journal of the Physical Sciences, 6(6), 2011, 1306-1316.
- A. Dubey, Artificial neural network models for rainfall prediction in Pondicherry, International Journal of Computational Application, 120(3), 2015, 30-35.
- M. Mustafa, R. Rezaur, S. Saiedi, H. Rahardjo and M. Isa, Evaluation of MLP-ANN training algorithms for modelling soil pore-water pressure responses to rainfall, Journal of Hydrologic Engineering, 18(1), 2013, 50-57.
- M. Kaltech, Rainfall-runoff modelling using artificial neural networks: modelling and understanding, Caspian Journal of Environmental Sciences, 6(1), 2008, 53-58.
- M. Awad, H. Pomares, I. Rojas, O. Salameh and M. Hamdon, Prediction of time series using RBF neural networks: a new approach of clustering, The International Arab Journal of Information Technology, 6(2), 2009, 138-144.
- J. Chen and B. J. Adams, Integration of artificial neural networks with conceptual models in rainfallˇrunoff modelling, Journal of Hydrology, 318(1-4), 2006, 232-249.
- X. Ren, X. Li, K. Ren, J. Song, Z. Xu, K. Deng and X. Wang, Deep learning-based weather prediction: a survey, Big Data Research, 23, 2020, Paper ID 100178.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 96
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 1