Quantification of the predictive uncertainty of artificial neural network based river flow forecast models

2012 ◽  
Vol 27 (1) ◽  
pp. 137-146 ◽  
Author(s):  
K. S. Kasiviswanathan ◽  
K. P. Sudheer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Flow ◽  
Predictive Uncertainty ◽  
Forecast Models ◽  
Artificial Neural ◽  
Flow Forecast

scholarly journals IMPLEMENTASI BACKPROPAGATION NEURAL NETWORK DALAM PRAKIRAAN CUACA DI DAERAH BALI SELATAN

2016 ◽  
Vol 5 (4) ◽  
pp. 126 ◽  
Author(s):  
I MADE DWI UDAYANA PUTRA ◽  
G. K. GANDHIADI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Weather Forecast ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Backpropagation Learning

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.

scholarly journals A multi-stage methodology for selecting input variables in ANN forecasting of river flows

2017 ◽  
Vol 19 (1) ◽  
pp. 49-57 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Atmospheric Circulation ◽  
River Flow ◽  
Neural Network Models ◽  
Prediction Ability ◽  
River Flows ◽  
Multi Stage ◽  
Artificial Neural ◽  
Input Variables

<p>The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under various patterns arising from the need of data downscaling for climate change predictions in hydrology domain. This paper presents the results of a novel multi-stage methodology for selecting input variables used in artificial neural network (ANN) models for river flow forecasting. The proposed methodology makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions, etc), but also in terms of inputs selection. The latter include various previous time steps of daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data and time expressed via functions of sine and cosine. Additionally, the no-linear behavior between river flow and the respective inputs is investigated by the ANN configuration itself and not only by correlation indices (or other equivalent contingency tools). The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a genetic algorithm (GA)-optimized single input ANN).&nbsp;</p>

scholarly journals River Flow Forecasting using artificial neural network (Shoor Ghaen)

2015 ◽  
Vol 37 ◽  
pp. 207
Author(s):  
Mohsen Rezaei ◽  
Ahmad Ali Akbari Motlaq ◽  
Ali Rezvani Mahmouei ◽  
Seyed Hojjatollah Mousavi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
River Flow ◽  
Uncertain System ◽  
Control Surface ◽  
Evaluation Phase ◽  
Artificial Neural ◽  
Precipitation Phenomena

In our country, most of the rivers located in dry and warm climate areas are seasonal, and many of them have experienced floods. That, along with concerns about scarcity of water resources and the need to control surface water, makes identification, modeling, and simulation of rivers’ behavior, necessary for to long-term planning and proper and rational use of river flows potential. Rainfall phenomenon and the resulting runoff in watersheds, as well as predicting them are of nonlinear system types. Artificial neural networks are able to analyze and simulate phenomena in nonlinear and uncertain system where the relationship between the components and system parameters are not well known or describable. Shoor Ghayen River, with 100 km length is the biggest seasonal river of Qaenat city and the main source of water in Farrokhi storage dam. Therefore, in this study according to the rainfall and runoff statistic of Khonik Olya hydrometric and Ghayen synoptic stations between 1976-1977 and 2010-2011 water years, precipitation phenomena and river runoff was predicted. MATLAB software is used to perform calculations. For modeling artificial neural network, 85 percent of data were used for training the proposed method, the remaining 15% were used for validating the method using 10 neurons, and a network with an error of less than 5% was developed for each month. The maximum correlation in evaluation phase was for April with the value of 0.99, and the minimum was for June and August with a value of 0.92. Overall results indicate optimum performance of artificial neural networks in predicting runoff caused by rainfall. It is also found that better results can be achieved by standardizing the data.

scholarly journals A time delay artificial neural network approach for flow routing in a river system

2006 ◽  
Vol 3 (5) ◽  
pp. 2735-2756 ◽  
Author(s):  
M. J. Diamantopoulou ◽  
P. E. Georgiou ◽  
D. M. Papamichail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Delay ◽  
Cross Correlation ◽  
River Flow ◽  
Flow Routing ◽  
Wide Range ◽  
Input Layer ◽  
Artificial Neural ◽  
Time Lagged

Abstract. River flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications.

River flow forecasting and estimation using different artificial neural network techniques

2008 ◽  
Vol 39 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Özgür Kişi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
River Flow ◽  
Streamflow Forecasting ◽  
Black Sea Region ◽  
Generalized Regression Neural Networks ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

This paper demonstrates the application of different artificial neural network (ANN) techniques for the estimation of monthly streamflows. In the first part of the study, three different ANN techniques, namely, feed forward neural networks (FFNN), generalized regression neural networks (GRNN) and radial basis ANN (RBF) are used in one-month ahead streamflow forecasting and the results are evaluated. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. Based on the results, the GRNN was found to be better than the other ANN techniques in monthly flow forecasting. The effect of periodicity on the model's forecasting performance was also investigated. In the second part of the study, the performance of the ANN techniques was tested for river flow estimation using data from the nearby river.

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