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.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

scholarly journals Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

2010 ◽  
Vol 61 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Perumal Chandrasekar ◽  
Vijayarajan Kamaraj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Voltage Sag ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

2005 ◽  
Vol 32 (4) ◽  
pp. 644-657 ◽  
Author(s):  
Ayman Ahmed Seleemah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Experimental Work ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Transverse Reinforcement ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

Investigation of Applying Physics Informed Neural Networks (PINN) and Variants on 2D Aerodynamics Problems

2020 ◽  
Author(s):  
Wee-Beng Tay ◽  
Murali Damodaran ◽  
Zhi-Da Teh ◽  
Rahul Halder
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Training Data ◽  
Batch Size ◽  
Test Case ◽  
Good Prediction ◽  
Time Varying ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Investigation of applying physics informed neural networks on the test case involving flow past Converging-Diverging (CD) Nozzle has been investigated. Both Artificial Neural Network (ANN) and Physics Informed Neural Network (PINN) are used to do the training and prediction. Results show that Artificial Neural Network (ANN) by itself is already able to give relatively good prediction. With the addition of PINN, the error reduces even more, although by only a relatively small amount. This is perhaps due to the already good prediction. The effects of batch size, training iteration and number of epochs on the prediction accuracy have already been tested. It is found that increasing batch size improves the prediction. On the other hand, increasing the training iteration may give poorer prediction due to overfitting. Lastly, in general, increasing epochs reduces the error. More investigations should be done in the future to further reduce the error while at the same time using less training data. More complicated cases with time varying results should also be included. Extrapolation of the results using PINN can also be tested.

scholarly journals Private Label and Macroeconomic Indexes: An Artificial Neural Networks Application

2020 ◽  
Vol 10 (17) ◽  
pp. 6043 ◽  
Author(s):  
Eloy Gil-Cordero ◽  
Juan-Pedro Cabrera-Sánchez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Private Label ◽  
Macroeconomic Variables ◽  
Macroeconomic Indicators ◽  
Per Capita ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Macroeconomic Indicator

Retail companies operate with a private label assortment of 40–45% of their total assortment, which has led to a significant growth of private labels in recent years in their countries of origin; however, when retail companies decide to internationalize, it is important to know which macroeconomic indicators are more relevant when entering a new country or continent. For that reason, in this study we have as a main objective to establish which are the most transcendental macroeconomic variables for the volume and value of the private label. For this purpose, we have analyzed a total of 1400 samples, creating an artificial neural network (ANN). The results show that the most important macroeconomic indicator that must be taken into consideration above other macroeconomic indicators for retail companies to be successful within a country is the per capita debt. In addition, we have considered in this research that unemployment is not the most important primary indicator for the volume of the private label.

Prediction of Dynamic Impedances Functions Using an Artificial Neural Network (ANN)

2012 ◽  
Vol 170-173 ◽  
pp. 3588-3593
Author(s):  
Sbartai Badreddine ◽  
Kamel Goudjil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Properties ◽  
Surface Foundation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Impedance Functions

Artificial Neural Networks (ANN) has seen an explosion of interest over the last few years. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. Hence, the main objective of this paper is to develop a model to predict the response of the soil-structure interaction system without using the calculate code based on sophisticate numerical methods by the employment of a statistical approach based on an Artificial Neural Network model (ANN). In this study, a data base which relates the impedance functions to the geometrics characteristic of the foundation and the dynamic properties of the soil is implemented. This leads to develop a neural network model to predict impedances functions (all modes) of a rectangular surface foundation. Then the results are compared with unused data to check the ANN model’s validity.

ECG SIGNALS PROCESSING WITH NEURAL NETWORKS

1995 ◽  
Vol 03 (04) ◽  
pp. 419-430 ◽  
Author(s):  
JOSE R. HILERA ◽  
VICTOR J. MARTÍNEZ ◽  
MANUEL MAZO
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Clinical Diagnosis ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Nowadays, the digital register process of electrocardiographical signals (ECG) constitutes a common practice for the diagnosis and controlling of patients suffering from cardiac disorders. In this paper we study the usefulness of Artificial Neural Network (ANN) for clinical diagnosis through the detection of arrythmias and the reduction of the large spaces occupied by ECG records.

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 Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

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