scholarly journals Rainfall Prediction Using Teleconnection Patterns Through the Application of Artificial Neural Networks

10.5772/35512 ◽  
2012 ◽  
Author(s):  
Gholam Abbas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Teleconnection Patterns ◽  
Rainfall Prediction ◽  
Artificial Neural

Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

2021 ◽  
Author(s):  
Mateus Alexandre da Silva ◽  
Marina Neves Merlo ◽  
Michael Silveira Thebaldi ◽  
Danton Diego Ferreira ◽  
Felipe Schwerz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Back Propagation ◽  
Metropolitan Region ◽  
Climate Variables ◽  
Back Propagation Algorithm ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

scholarly journals APPLICATION OF BACKPROPAGATION NEURAL NETWORKS IN PREDICTING RAINFALL DATA IN AMBON CITY

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Yopi Andry Lesnussa ◽  
C. G. Mustamu ◽  
F. Kondo Lembang ◽  
M. W. Talakua
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Life ◽  
Prediction System ◽  
Air Velocity ◽  
Rainfall Prediction ◽  
Accuracy Level ◽  
Backpropagation Method ◽  
Iteration Number ◽  
Artificial Neural

The Artificial Neural Networks is a process of information system on certain traits which as representatives of the human neural networks. The Artificial Neural Networks can be applied in every area of human life, one of them is environment especially about prediction of climate or weather. In this research, the artificial neural network is used to predict the rainfall with Backpropagation method and using MATLAB software. The other meteorology parameters used to predict the rainfall are air temperature, air velocity and air pressure. The result showed less accuracy level is 80% by using alpha 0,7, iteration number (epoch) 10000 and MSE value = 0,0218. Therefore, the result of rainfall prediction system is accurate.

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