Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

2019 ◽  
Vol 167 ◽  
pp. 105080 ◽  
Author(s):  
Regiane Souza Vilanova ◽  
Sidney Sara Zanetti ◽  
Roberto Avelino Cecílio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Atlantic Rainforest ◽  
Input Output ◽  
Daily Streamflow ◽  
Artificial Neural ◽  
Brazilian Atlantic Rainforest

scholarly journals Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River

2013 ◽  
Vol 15 (3) ◽  
pp. 1022-1041 ◽  
Author(s):  
R. Maheswaran ◽  
Rakesh Khosa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Model ◽  
Volterra Model ◽  
Lead Times ◽  
Discrete Wavelet ◽  
Promising Alternative ◽  
Daily Streamflow ◽  
Non Linear ◽  
Artificial Neural

In this study, a multi-scale non-linear model based on coupling a discrete wavelet transform (DWT) and the second-order Volterra model, i.e. the wavelet Volterra coupled (WVC) model, is applied for daily inflow forecasting at Krishna Agraharam, Krishna River, India. The relative performance of the WVC model was compared with regular artificial neural networks (ANN), wavelet-artificial neural networks (WA-ANN) models and other baseline models such as auto-regressive moving average with exogenous variables (ARMAX) for lead times of 1–5 days. The models were applied for the forecasting of daily streamflow at Krishna Agraharam Station at Krishna River. The WVC performed very well, especially when compared with the WA-ANN model for lead times of 4 and 5 days. The results indicate that the WVC model is a promising alternative to the other traditional models for short-term flow forecasting.

Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting

2012 ◽  
Vol 39 (4) ◽  
pp. 402-414 ◽  
Author(s):  
Mehdi Vafakhah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Arma Model ◽  
Streamflow Forecasting ◽  
Inference System ◽  
Daily Streamflow ◽  
Neuro Fuzzy ◽  
Artificial Neural

The present article aims to forecast streamflow by using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and autoregressive moving average (ARMA). For this purpose, the daily streamflow time series of two hydrometry stations of Hajighoshan and Tamar on Gorgan River are used for two periods of 1983–2007 and 1974–2007, respectively. Root mean square error (RMSE) and correlation coefficient (R) statistics are employed to evaluate the performance of the ANNs, ANFIS, and ARMA models for forecasting streamflow (1 day ahead). Comparison of the results reveals that the ANFIS model outperforms the ARMA model. Based on the results of validation stage, for the forecasting 1 day ahead streamflow, ANN with RMSE = 0.028 m3/s and R = 0.59 for the Hajighoshan station and RMSE = 0.013 m3/s and R = 0.44 for the Tamar station were found to be superior to the ANFIS with RMSE = 1.98 m3/s and R = 0.42 for the Hajighoshan station and RMSE = 2.18 m3/s and R = 0.22 for the Tamar station. In addition, for 2 day and 3 day ahead streamflow forecasts, the ANN models show superiority in the accuracy of forecasting streamflow compared with the ANFIS models.

scholarly journals IDENTIFIKASI POLA KARAKTER ANAK DENGAN ALGORITMA PERCEPTRON

2019 ◽  
Vol 3 (1) ◽  
pp. 86
Author(s):  
Sri Rahmadhany
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Computational Method ◽  
Input Output ◽  
Perceptron Algorithm ◽  
Behavioral Variables ◽  
Artificial Neural

Abstract - Artificial Neural Network is a computational method that works like a human brain. The Perceptron algorithm is one method that exists in Artificial Neural Networks. The research carried out was the identification of children's character patterns using the Perceptron algorithm. The Perceptron algorithm is very reliable in recognizing patterns, one of which is the child's character pattern as was done in this study. The Perceptron algorithm identifies the character patterns of children through three inputs and two outputs. The three outputs are taken from nature variables, attitude variables and behavioral variables. The output is four human temperaments according to Hipocrates, namely sanguin, melancholy, choleric and plegamatic. All inputs and outputs will be converted into binary numbers to be trained with Matlab software.Keywords - Artificial Neural Networks, Perceptron Algorithms, child character patterns, input, output, binary numbers. Abstrak - Jaringan Syaraf Tiruan merupakan salah satu metode komputasi yang dapat bekerja seperti layaknya otak manusia. Algortima Perceptron merupakan salah satu metode yang ada pada Jaringan Syaraf Tiruan. Penelitian yang dilakukan adalah identifikasi pola karakter anak dengan menggunakan algoritma Perceptron. Algoritma Perceptron sangat handal dalam mengenali pola salah satunya yaitu pola karakter anak seperti yang dilakukan dalam penelitian ini. Algoritma Perceptron mengidentifikasi pola karakter anak melalui tiga input dan dua output. Tiga output tersebut diambil dari variabel sifat, variabel sikap dan variabel tingkah laku. Adapun output merupakan empat temperamen manusia menurut Hipocrates yaitu sanguin, melankolis, koleris dan plegamatis. Seluruh input dan output akan diubah menjadi bilangan biner untuk dilatih dengan software Matlab.Kata Kunci - Jaringan Syaraf Tiruan, Algoritma Perceptron, pola karakter anak, input, output, bilangan biner.

Learning in Feed-Forward Artificial Neural Networks II

2011 ◽  
pp. 1012-1017
Author(s):  
Lluís A. Belanche Muñoz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Output ◽  
Rbf Networks ◽  
Learning Rules ◽  
The Neural Network ◽  
Artificial Neural ◽  
Considerable Work ◽  
Derivative Information

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Other kinds of supervised ANN have developed their own techniques. Such is the case of Radial Basis Function (RBF) networks (Poggio & Girosi, 1989). There has been also considerable work on the development of adhoc learning methods based on evolutionary algorithms.

scholarly journals Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach

2021 ◽  
pp. 29-55
Author(s):  
Roberto Louis Forestal ◽  
Shih-Ming Pi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Food System ◽  
Commodity Price ◽  
Optimal Scaling ◽  
Economic Approach ◽  
Pricing Model ◽  
Input Output ◽  
Ecological Economic ◽  
Artificial Neural

This research paper employs input-output pricing model based on ecological-economic approach to investigate the impacts of internal factors as well as external forces on agriculture commodities. To empirically test our model, we select two different methodologies such as the optimal scaling regression with nonlinear transformations and feedforward artificial neural networks. Our sample includes data related to price of agriculture and energy commodities (cocoa, coffee and crude oil), production of crops and livestock, emissions of greenhouse gases (GHG) from agriculture from 1961 to 2019. Results find a bidirectional relationship between cocoa price and coffee price explaining by the fact that commodity-dependent countries often use kindred production landscapes and similar supply chain management when dealing with coffee and cocoa. Therefore, effect of supply side shocks may be transmitted from one market to another. We also present evidence that greenhouse gas emissions have strong effect on commodity price, thus we encourage an integrated approach including both concrete technological and proactive managerial measures in order to mitigate global warming impacts on the food system. We believe that these findings will be of interest to commodity producers, asset managers and academics who look a better understanding of the dynamics of commodity markets. JEL classification numbers: C50, Q02, Q57. Keywords: Agriculture commodity, Input-output pricing model, Ecological-economic approach, Artificial neural networks, Optimal scaling regression.

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