A new hybrid hydrologic model of XXT and artificial neural network for large-scale daily runoff modeling

Abstract. Rainfall simulation is a method of obtaining precipitation data on rainfall station based on precipitation data of other stations in same watershed at the same time, using a linear mathematical model constructed by Artificial Neural Network (ANN) method. The application of the simulation result is useful to provide information for the decision-makers. ANN backpropagation method is the one used in modeling the rainfall in a watershed because it can solve a complex mathematic problem. The objective of research was to find out the hydrologic model and its application to predict precipitation data at Bah Bolon watershed in the future. The input variable of research was data of rain at Bah Jambi rainfall station. The parameters used in this research were Mean Squared Error (MSE)= 0.028, epoch= 1000 iteration, hidden layer number= 2, neuron hidden layer number= 3, momentum= 0.7, learning rate: 0.9, training period= 4 years. The result of model verification shows the very strong correlation between simulated rain data and actual rain data, with score of 0.9664, and the reliability of hydrologic system model at Bah Bolon watershed is 64.48%.

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ANNUAL RAINFALL-RUNOFF MODELING OF HARNAV WATERSHED OF A SABARMATI RIVER BASIN, INDIA USING ARTIFICIAL NEURAL NETWORK

International Journal of Advance Engineering and Research Development ◽

10.21090/ijaerd.ncan01 ◽

2017 ◽

Vol 4 (02) ◽

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

River Basin ◽

Annual Rainfall ◽

Rainfall Runoff ◽

Runoff Modeling ◽

Sabarmati River ◽

Artificial Neural

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OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD

SINERGI ◽

10.22441/sinergi.2018.3.008 ◽

2018 ◽

Vol 22 (3) ◽

pp. 193

Author(s):

Ika Sari Damayanthi Sebayang ◽

Agus Suroso ◽

Alnis Gustin Laoli

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Urban Areas ◽

Back Propagation ◽

Water Volume ◽

Rainfall Runoff ◽

Back Propagation Algorithm ◽

Runoff Modeling ◽

Artificial Neural ◽

The Relationship

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 R2: January = 0,96937; February = 0,92984; March = 0,90666; April = 0,92566; May = 0,9128; June = 0,87975; July= 0,85292; August = 0,95943; September = 0,88229; October = 0,90537; November = 0,93522; December = 0,9111. with MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

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