scholarly journals Application of artificial neural networks (ANN) in Lake Drwęckie water level modelling

2015 ◽  
Vol 15 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Adam Piasecki ◽  
Jakub Jurasz ◽  
Rajmund Skowron
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Meteorological Data ◽  
Water Level Fluctuations ◽  
Explanatory Variables ◽  
Real Value ◽  
The Subject ◽  
Artificial Neural ◽  
Hidden Layer

Abstract This paper presents an attempt to model water-level fluctuations in a lake based on artificial neural networks. The subject of research was the water level in Lake Drwęckie over the period 1980-2012. For modelling purposes, meteorological data from the weather station in Olsztyn were used. As a result of the research conducted, the model M_Meteo_Lag_3 was identified as the most accurate. This artificial neural network model has seven input neurons, four neurons in the hidden layer and one neuron in the output layer. As explanatory variables meteorological parameters (minimal, maximal and mean temperature, and humidity) and values of dependent variables from three earlier months were implemented. The paper claims that artificial neural networks performed well in terms of modelling the analysed phenomenon. In most cases (55%) the modelled value differed from the real value by an average of 7.25 cm. Only in two cases did a meaningful error occur, of 33 and 38 cm.

scholarly journals FORECASTING SURFACE WATER LEVEL FLUCTUATIONS OF LAKE SERWY (NORTHEASTERN POLAND) BY ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION

2017 ◽  
Vol 25 (4) ◽  
pp. 379-388 ◽  
Author(s):  
Adam PIASECKI ◽  
Jakub JURASZ ◽  
Rajmund SKOWRON
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Level ◽  
Water Level Fluctuations ◽  
Temperate Climate ◽  
Binary Variable ◽  
Northeastern Poland ◽  
Artificial Neural

The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks

2015 ◽  
Vol 744-746 ◽  
pp. 1938-1942
Author(s):  
Yi He ◽  
Duan Feng Chu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comfort Level ◽  
Subjective Rating ◽  
Acceleration Data ◽  
Acceleration And Deceleration ◽  
The Subject ◽  
Artificial Neural ◽  
The Relationship ◽  
Subjective Sensation

As the siginificant factors influence passengers comfort, the vehicle celebration performance may easy to cause accidents, such as hard acceleration and deceleration performance. In order to find the relationship between passengers comfort and celebration performance, 35 passengers and three professional drivers were recruited in the field experiment. The passengers’ comfort feelings were analysed by subject questionnaires, the acceleration and deceleration data were received by CAN bus.The Artificial Neural Networks (ANNs) model was elaborated to estimate and predict the passengers comfort level of driver unsafe acceleration behavior situations. Therefore, the subject views of the passengers could be compared to object acceleration data. An ANN is applied to interconnect output data (subjective rating) with input data (objective parameters). Finally, it is found the investigatioin have demonstrated that the objective values are efficiently correlated with the subjective sensation. Thus, the presented approach can be effectively applied to support the drive train development of bus.

scholarly journals Climate change or regional human impacts? remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem

2020 ◽  
Author(s):  
Ehsan Foroumandi ◽  
Vahid Nourani ◽  
Elnaz Sharghi
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Positive Trend ◽  
Water Level Change ◽  
Lake Urmia ◽  
Level Change ◽  
Artificial Neural

Abstract Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmental variables over the Lake Urmia basin using remote sensing tools, artificial neural networks, wavelet transforms, and Mann–Kendall trend tests from 1995 to 2019 in order to determine the primary reason of the decline and to find the most important hydrologic periodicities over the basin. The results indicated that for the monthly-, seasonally-, and annually-based time series, the components with 4-month and 16-month, 24- and 48-month, and 2- and 4-year, respectively, are the most dominant periodicities over the basin. The agricultural increase according to the vegetation index and evapotranspiration and their close relationship with the water-level change indicated that human land-use is the main reason for the decline. The increasing agriculture, in the situations that the precipitation has not increased, caused the inflow runoff to the lake to decline and the remaining smaller discharge is not sufficient to stabilize the water level. Temperature time series, also, has experienced a significant positive trend which intensified the water-level change.

scholarly journals Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Francisco J. Diez ◽  
Luis M. Navas-Gracia ◽  
Leticia Chico-Santamarta ◽  
Adriana Correa-Guimaraes ◽  
Andrés Martínez-Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Modelling ◽  
Solar Irradiation ◽  
Data Series ◽  
Markov Analysis ◽  
Global Solar Irradiation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

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