Open Access Open Access  Restricted Access Subscription or Fee Access

Prediction of Rainfall Using MLP and RBF Networks for Monsoon and Post-Monsoon Seasons of Pune and Mahabaleshwar Regions

N VIVEKANANDAN

Abstract


Prediction of rainfall for a river basin has utmost importance for planning of irrigation and drainage system as also for command area development. For example, analysis of consecutive days of rainfall is more relevant for drainage design of agricultural lands whereas analysis of weekly rainfall data is relevant for planning of cropping pattern.  Likewise, analysis of monthly and seasonal data is more useful for water management practices. With the development of Artificial Intelligence (AI), a number of various AI methods such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System, Fuzzy Logic, Support Vector Machine and Evolutionary Optimization Algorithm are widely applied for prediction of rainfall. The ANN represents a complex non-linear relationship and extract the dependence between variables through the training process and hence used. In this paper, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are used for training the network data. The performance of the MLP and RBF networks adopted in rainfall prediction has been evaluated by model performance indicators viz., correlation coefficient, Nash–Sutcliffe model efficiency and root mean squared error. The outcomes of the results of the study shows that the RBF is better suited amongst two networks studied for prediction of rainfall for monsoon and post-monsoon seasons of Pune and Mahabaleshwar regions. 

Full Text:

PDF

References


. Li M & Shao Q. An improved statistical approach to merge satellite rainfall estimates and rain gauge data. Journal of Hydrology, 2010, 385(1-4): 51–64.

. Calvello M, Cascini L & Sorbino G. A numerical procedure for predicting rainfall-induced movements of active landslides along pre-existing slip surfaces. International Journal for Numerical and Analytical Methods in Geomechanics, 2008, 32(4): 327–351.

. Cramer S, Kampouridis M, Freitas AA & Alexandridis AK. An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 2017, 85(November issue): 169–181.

. Al Mamun A, Bin Salleh MN & Noor HM. Estimation of short-duration rainfall intensity from daily rainfall values in Klang valley, Malaysia. Applied Water Science,2018, 8(7): 1-10.

. Singh P. Indian summer monsoon rainfall forecasting using time series data: A fuzzy-entropy-neuro based expert system. Geoscience Frontiers, 2017, 9(4): 1243-1257.

. Ko CM, Jeong YY, Lee YM & Kim BS. The development of a quantitative precipitation forecast correction technique based on machine learning for hydrological applications. Atmosphere, 2020, 11(1): 1-17.

. Senthil Kumar AR, Sudheer KP, Jain SK & Agarwal, PK. Rainfall–runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes, 2004, 19(6): 1277–1291.

. Chattopadhyay S. Feed forward artificial neural network model to predict the average summer monsoon rainfall in India. Acta Geophysica, 2007, 55(3): 369–382.

. Dahamsheh A & Aksoy H. Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteorological Applications, 2009, 16(3): 325–337.

. Dastorani MT, Afkhami H, Sharifidarani H & Dastorani M. Application of ANN and ANFIS models on dryland precipitation prediction (Case study: Yazd in Central Iran). Journal of Applied Sciences, 2010, 10(20): 2387–2394.

. Sharma MA & Singh JB. Comparative study of rainfall forecasting models. New York Sciences Journal, 2011, 4(7): 115-120.

. Azadi S & Sepaskhah AR. Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theoretical and Applied Climatology, 2012, 109(1–2): 175–189.

. Nayak DR, Mahapatra A & Mishra P. A Survey on Rainfall Prediction using Artificial Neural Network. International Journal of Computer Applications, 2013, 72(16): 32-40.

. Mislan H, Hardwinarto S & Sumaryono MA. Rainfall monthly prediction based on artificial neural network: A case study in Tenggarong Station, East Kalimantan-Indonesia. Procedia Computer Science, 2015, 59: 142-151.

. Choubin B, Malekian S & Golshan M. Application of several data-driven techniques to predict a standardized precipitation index. Atmósfera, 2016, 29(2): 121-128.

. Lee S, Kim JC, Jung HS, Lee MJ & Lee S. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk, 2017, 8(2); 1185–1203.

. Sofian IM, Affandi AK, Iskandar I & Apriani Y. Monthly rainfall prediction based on artificial neural networks with back propagation and radial basis function. International Journal of Advances in Intelligent Informatics, 2018, 4(2): 154-166.

. Satish P, Srinivasulu S & Swathi R. A Hybrid Genetic Algorithm Based Rainfall Prediction Model Using Deep Neural Network. International Journal of Innovative Technology and Exploring Engineering, 2019, 8(12): 5370-5373.

. Zhang X, Mohanty SN, Parida AK, Pani SK, Dong B & Cheng X. Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access, 2020, 8: 30223-30233.

. Tokar S & Markus, M. Precipitation runoff modeling using artificial neural network and conceptual models. Journal of Hydrologic Engineering, 2000, 5(2): 156-161.

. Ddubey A. Artificial neural network models for rainfall prediction in Pondicherry. International Journal of Computational Application, 2015, 120(3): 30–35.

. Mustafa M, Rezaur R, Saiedi S, Rahardjo H & Isa M. Evaluation of MLP-ANN training algorithms for modelling soil pore-water pressure responses to rainfall. Journal of Hydrologic Engineering, 2013, 18(1): 50-57.

. Kaltech M. Rainfall-runoff modelling using artificial neural networks: modelling and understanding. Caspian Journal of Environmental Sciences, 2008, 6(1): 53-58.

. Chen J & Adams BJ. Integration of artificial neural networks with conceptual models in rainfall-runoff modelling. Journal of Hydrology, 2006, 318(1-4): 232-249.

. Vivekanandan N. Prediction of Rainfall Using MLP and RBF Networks. International Journal of Advanced Networking and Applications, 2014, 5(4):1974-1979.




DOI: https://doi.org/10.37628/jwre.v7i1.675

Refbacks

  • There are currently no refbacks.