[PDF] Top 20 Classification of hydro meteorological conditions and multiple artificial neural networks for streamflow forecasting
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Classification of hydro meteorological conditions and multiple artificial neural networks for streamflow forecasting
... the networks are fed by both past flows and past precipitation observations: extremely encouraging results have been ob- tained in literature on both real and synthetic rainfall-runoff data (among the many others, ... See full document
12
Overview of the Artificial Neural Networks and Fuzzy Logic Applications in Operational Hydrological Forecasting Systems
... those conditions and because building new flood defences structures for defending vulnerable areas has serious financial implications, the timely forecasting of floods is becoming more important for flood ... See full document
6
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
... synthetic streamflow generation and streamflow forecasting include simple and multiple linear regression, autoregressive moving average (ARMA) models, ARMA with exogenous variables (ARMAX) and ... See full document
14
Artificial Neural Network Models Investigation for Euphrates River Forecasting & Back Casting
... multilayer networks and also can be categorized into feed forward and feed backward networks due to the direction of the information and processing (Haddad et ... See full document
15
Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)
... the Artificial Neural Networks (ANNs) and the Autoregressive Moving Averages (ARMA) in forecasting the weekly amounts of solid waste generated by single persons in fourteen households of Kator ... See full document
13
Using the urban landscape mosaic to develop and validate methods for assessing the spatial distribution of urban ecosystem service potential
... operation revealed that people living in areas characterised as ‘detached’ frequently complained about local problems they experienced from trees such as leaves blocking drains and sunlight, and making footpaths ... See full document
294
Application of artificial neural networks on drought prediction in Yazd (Central Iran)
... geographical longitude of 54º, 17´ and latitude of 31º, 54´ with a hyper arid climate condition according to the extended Demartonn climatic classification. Various combinations of climate factors including ... See full document
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Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation
... recurrent neural networks, two- and three-dimensional feature tensors can also be inputted to the ...convolutional neural network (CNN), the input is a three-dimensional tensor, where the value of ... See full document
13
Blind Navigation System using Artificial Intelligence
... This project contains three main parts, a raspberry pi 3 (powered by android things), camera and artificial intelligence. When the person presses the button on device, the camera module starts to take a pictures ... See full document
5
The Usefulness of Artificial Neural Networks in Forecasting Exchange Rates
... Because exchange rates are influenced by many economic, political and psychological factors, it has been hard to identify a unique economic model that can provide reliable forecasts. Some authors state that “the poor ... See full document
8
Network Data Classification through Artificial Neural Networks and GenClust++ Algorithm
... The prediction accuracy may be altered by the presence of irrelevant or redundant attributes. We will perform two types of feature selection in order to improve the classification accuracy and the total ... See full document
8
Artificial Neural Networks in the Demand Forecasting of a Metal Mechanical Industry
... However, because effective strategic planning, both in the short and long term, depends on a forecast of demand. It is where the use of new techniques has raised satisfactory solutions such as the use of ... See full document
7
Advanced approach to numerical forecasting using artificial neural networks
... used. Generally are the methods divided into tech- niques based on machine learning (Mitchell, 1997) (e.g. Support Vector Machines, Decision Trees) and techniques inspired by biological processes (Mařík, Štěpánková, ... See full document
8
FORECASTING OF DAILY NEED PRODUCT USING ARTIFICIAL NEURAL NETWORKS
... GFF networks, train certain output nodes to respond to certain input patterns and the changes in connection weights, due to learning, cause those same nodes to respond to more general classes of ... See full document
8
Assessment of Severity Level for Diabetic Macular Oedema Using Machine Learning Algorithms
... After segmenting the abnormal portion of an input image, Grey Level Co-occurrence Matrix extracts features from the spatial relationship of the pixel, which is important for classification of abnormalities. ... See full document
8
Trends of hydro meteorological data and impact of climate change on the streamflow of gilgel gibe 1 river basin Ethiopia
... affect the intended purpose water resources projects, which have been designed, based on the historically recorded data. Therefore, the objective of this study was to analyze the trend of the hydro- ... See full document
6
Evaluation of Artificial Neural Networks in Foreign Exchange Forecasting
... makers. Forecasting with a very week tools has an adverse effect on the economic development due to its negative effect on international trade and investment, using a weak model to forecast will lead to taking a ... See full document
8
A hybrid approach based on arima and artificial neural networks for crime series forecasting
... series forecasting is an active domain of research that has become increasingly important in various fields of research, such as business, economics, finance, science and ...series forecasting, the data ... See full document
23
COMPARATIVE ANALYSIS OF THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS (ANNs) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS ON RAINFALL FORECASTING
... Delay Neural Network (FTDNN) to make one-step-ahead ...feed-forward neural networks and time delay neural networks were found to capture the dynamic structure of the rainfall process ... See full document
6
Hybrid Network of Neuro Fuzzy based Decision Tool for Stock Market Analysis
... financial forecasting is not restricted only to the technical analysis approach, but has also been applied to the fundamental ...fuzzy neural network is trained with additional political, financial, ... See full document
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