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Tytuł:
Information potential of the spectral response of Polish soils, in the NIR range, in the light of lucas database analyses. Soil properties vector model
Autorzy:
Gruszczyński, Stanisław
Powiązania:
https://bibliotekanauki.pl/articles/101552.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Stowarzyszenie Infrastruktura i Ekologia Terenów Wiejskich PAN
Tematy:
near infrared spectroscopy
soil properties prediction
machine learning model
Opis:
The paper presents simple machine learning models used for prediction of some soil properties based on the NIR spectral response. Data on mineral soils from Poland were taken from the LUCAS dataset. Machine learning model was used that is included in the category of so-called multilayer perceptron (MLP). The MLP model input was a vector of combined, transformed inputs made by means of the PLSR (partial last squares regression) algorithm (45 inputs in total). The output was a vector of properties, reduced to 9 components due to poor modelling effects of the P and K components. The estimation errors for texture, soil organic carbon (SOC) and carbonates can be considered acceptable from the point of view of their suitability in the development of cartographic documentation. It can be supposed that further regionalization will improve these results.
Źródło:
Infrastruktura i Ekologia Terenów Wiejskich; 2019, II/1; 95-104
1732-5587
Pojawia się w:
Infrastruktura i Ekologia Terenów Wiejskich
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Climate Change and its Effect on the Energy Production from Renewable Sources – A Case Study in Mediterranean Region
Autorzy:
Gjika, Eralda
Basha, Lule
Sokoli, Arnisa
Powiązania:
https://bibliotekanauki.pl/articles/2202305.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
energy production
temperature
rainfall
CO2 emission
machine learning model
Opis:
In terms of climate forecasting, the Mediterranean region is among the most difficult. It is correlated with the five significant subtropical high pressure belts of the oceans and is symbolized by dry and hot summer and cold and rainy winter. Due to its location in the area, Albania is particularly susceptible to climatic changes. It has been noted that summertime sees the greatest temperature increases. More intense heat waves that stay longer and occur more frequently are anticipated in the eastern Mediterranean. The seasonal patterns of precipitation have not changed, but the amount of rain has become more intense. The effects of climate change have drawn attention to various renewable energy sources, including solar and wind power. In this study, the changes and prospective in average temperature, rainfall, humidity, CO2 emission and their impact in energy production were investigated. Several different models such as Auto Regressive Integrated Moving Average method; Prophet algorithm; Elastic-Net Regularized Generalized Linear Model; Random Forest Regression models; Prophet Boost algorithm; have been built for the study and prediction of each variable. The appropriate models are used to determine the anticipated values of the indicators for a period of four years. The prediction shows an increase in CO2 emission which leads to a decrease in energy production by hydropower. These findings suggest the use of other renewable sources for energy production in the country and the Mediterranean region.
Źródło:
Journal of Ecological Engineering; 2022, 23, 12; 285--298
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation of the depth of penetration in a plunging hollow jet using artificial intelligence techniques
Autorzy:
Bodana, D.
Tiwari, N. M.
Ranjan, S.
Ghanekar, U.
Powiązania:
https://bibliotekanauki.pl/articles/1818515.pdf
Data publikacji:
2020
Wydawca:
Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:
depth of penetration
machine learning model
classical models
plunging hollow jets
głębokość penetracji
model uczenia maszynowego
modele klasyczne
Opis:
Purpose: Experimental investigations assessment and comparison of different classical models and machine learning models employed with Gaussian process regression (GPR) and artificial neural network (ANN) in the estimation of the depth of penetration (Hp) of plunging hollow jets. Design/methodology/approach: In this analysis, a set of data of 72 observations is derived from laboratory tests of plunging hollow jets which impinges into the water pool of tank. The jets parameters like jet length, discharge per unit water depth and volumetric oxygen transfer coefficient (Kla20) are varied corresponding to the depth of penetration (Hp) are estimated. The digital image processing techniques is used to estimate the depth of penetration. The Multiple nonlinear regression is used to establish an empirical relation representing the depth of penetration in terms of jet parameters of the plunging hollow jets which is further compared with the classical equations used in the previous research. The efficiency of MNLR and classical models is compared with the machine learning models (ANN and GPR). Models generated from the training data set (48 observations) are validated on the testing data set (24 observations) for the efficiency comparison. Sensitivity assessment is carried out to evaluate the impact of jet variables on the depth of penetration of the plunging hollow jet. Findings: The experimental performance of machine learning models is far better than classical models however, MNLR for predicting the depth of penetration of the hollow jets. Jet length is the most influential jet variable which affects the Hp. Research limitations/implications: The outcomes of the models efficiency are based on actual laboratory conditions and the evaluation capability of the regression models may vary beyond the availability of the existing data range. Practical implications: The depth of penetration of plunging hollow jets can be used in the industries as well as in environmental situations like pouring and filling containers with liquids (e.g. molten glass, molten plastics, molten metals, paints etc.), chemical and floatation process, wastewater treatment processes and gas absorption in gas liquid reactors. Originality/value: The comprehensive analyses of the depth of penetration through the plunging hollow jet using machine learning and classical models is carried out in this study. In past research, researchers were used the predictive modelling techniques to simulate the depth of penetration for the plunging solid jets only whereas this research simulate the depth of penetration for the plunging hollow jets with different jet variables.
Źródło:
Archives of Materials Science and Engineering; 2020, 103, 2; 49--61
1897-2764
Pojawia się w:
Archives of Materials Science and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving Crop Yield Predictions in Morocco Using Machine Learning Algorithms
Autorzy:
Ed-Daoudi, Rachid
Alaoui, Altaf
Ettaki, Badia
Zerouaoui, Jamal
Powiązania:
https://bibliotekanauki.pl/articles/24202898.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
crop yield prediction
machine learning algorithm
statistical model
model evaluation
Opis:
In Morocco, agriculture is an important sector that contributes to the country’s economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers.
Źródło:
Journal of Ecological Engineering; 2023, 24, 6; 392--400
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining forecasts? Keep it simple
Autorzy:
Lis, Szymon
Chlebus, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/22443122.pdf
Data publikacji:
2023-10-31
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
Machine learning
GARCH model
combined forecasts
commodities
VaR
Opis:
This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR)modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 forgold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure. Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.
Źródło:
Central European Economic Journal; 2023, 10, 57; 343-370
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Degradation assessment of bearing based on machine learning classification matrix
Autorzy:
Kumar, Satish
Kumar, Paras
Kumar, Girish
Powiązania:
https://bibliotekanauki.pl/articles/1841739.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
degradation state
health condition indicator
machine learning
diagnostic model
prognostic model
Opis:
In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 2; 395-404
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Supervisory optimal control using machine learning for building thermal comfort
Autorzy:
Abdufattokhov, Shokhjakhon
Mahamatov, Nurilla
Ibragimova, Kamila
Gulyamova, Dilfuza
Yuldashev, Dilyorjon
Powiązania:
https://bibliotekanauki.pl/articles/2204083.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
building thermal comfort
Gaussian processes
machine learning
model predictive control
Opis:
For the past few decades, control and building engineering communities have been focusing on thermal comfort as a key factor in designing sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear and complex building dynamics characterised by the time-varying environment with disturbances. The primary focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes (GPs) and incorporating it into model predictive control (MPC) to minimise energy consumption and provide thermal comfort satisfaction. The full probabilistic capabilities of GPs are exploited from two perspectives: the mean prediction is used for the room temperature model, while the uncertainty is involved in the MPC objective not to lose the desired performance and design a robust controller. We illustrated the potential of the proposed method in a numerical example with simulation results.
Źródło:
Operations Research and Decisions; 2022, 32, 4; 1--15
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes
Autorzy:
Topór, Tomasz
Sowiżdżał, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/27310145.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
machine learning
model stacking
ensemble method
carbonates
seismic attributes
porosity prediction
Opis:
This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.
Źródło:
Geology, Geophysics and Environment; 2023, 49, 3; 245--260
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning predictive modeling of the price of cassava derivative (GARRI) in the South West Of Nigeria
Autorzy:
Olanloye, O.
Oduntan, E.
Powiązania:
https://bibliotekanauki.pl/articles/118266.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
fluctuation
prices
machine learning
predictive model
cassava derivative
fluktuacja
ceny
nauczanie maszynowe
model predykcyjny
pochodna manioku
Opis:
Fluctuation in prices of Agricultural products is inevitable in developing countries faced with economic depression and this, has brought a lot of inadequacies in the preparation of Government financial budget. Consumers and producers are poorly affected because they cannot take appropriate decision at the right time. In this study, Machine Learning(ML) predictive modeling is being implemented using the MATLAB Toolbox to predict the price of cassava derivatives (garri) in the South Western part of Nigeria. The model predicted that by the year 2020, all things being equal, the price of (1kg) of garri will be 500. This will boost the Agricultural sector and the economy of the nation.
Źródło:
Applied Computer Science; 2018, 14, 1; 53-63
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Iontophoresis of the eye - a computational approach
Jonoforeza oka –podejście obliczeniowe
Autorzy:
Mikołajewska, Emilia
Mikołajewski, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/41205874.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:
computational model
machine learning
iontophoresis
eye
drug delivery
model obliczeniowy
uczenie maszynowe
jonoforeza
oko
dostarczanie leków
Opis:
ontophoresis is an effective, non-invasive method of intraocular drug delivery based on electric current. However, it has many limitations that can be addressed by effective computational models based on both machine learning (a data-driven approach) and other artificial intelligence methods and techniques. To date, computational models using AI/ML are lacking, including for the iontophoresis mechanism itself. Their wider use would help facilitate the delivery of drugs to the eye, which remains a major challenge dueto the multiple barriers in the eye. The aim of this paper is to explore the feasibility of developing a computational model for ocular iontophoresis using available AI methods and techniques.
Jonoforeza jest skuteczną, nieinwazyjną metodą wewnątrzgałkowego podawania leków opartą na prądzie elektrycznym. Ma jednak wiele ograniczeń, które można rozwiązać za pomocą skutecznych modeli obliczeniowych opartych zarówno na uczeniu maszynowym (podejście oparte na danych), jak i innych metodach i technikach sztucznej inteligencji. Do tej pory brakuje modeli obliczeniowych wykorzystujących AI/ML, w tym dla samego mechanizmu jonoforezy. Ich szersze zastosowanie pomogłoby ułatwić dostarczanie leków do oczu, co pozostaje poważnym wyzwaniem ze względu na liczne bariery w oku. Celem artykułu jest zbadanie wykonalności opracowania modelu obliczeniowego dla jonoforezy ocznej przy użyciu dostępnych metod i technik sztucznej inteligencji.
Źródło:
Studia i Materiały Informatyki Stosowanej; 2023, 15, 1; 28-32
1689-6300
Pojawia się w:
Studia i Materiały Informatyki Stosowanej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models
Autorzy:
Drzewiecki, W.
Powiązania:
https://bibliotekanauki.pl/articles/145505.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
zmiany klimatyczne
modelowanie hydrologiczne
las
machine learning
model ensembles
sub-pixel classification
Landsat
Opis:
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
Źródło:
Geodesy and Cartography; 2016, 65, 2; 193-218
2080-6736
2300-2581
Pojawia się w:
Geodesy and Cartography
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
Autorzy:
Sumi, S. M.
Zaman, M. F.
Hirose, H.
Powiązania:
https://bibliotekanauki.pl/articles/331290.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maszyna ucząca się
metoda wielomodelowa
przetwarzanie wstępne
rainfall forecasting
machine learning
multi model method
preprocessing
model ranking
Opis:
In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping
Autorzy:
Drzewiecki, W.
Powiązania:
https://bibliotekanauki.pl/articles/145416.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
modele regresji
nieprzepuszczalność
subpiksel
impervious area
sub-pixel classification
machine learning
model ensembles
Landsat
Opis:
We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
Źródło:
Geodesy and Cartography; 2017, 66, 2; 171-209
2080-6736
2300-2581
Pojawia się w:
Geodesy and Cartography
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie rozmytej mapy kognitywnej w prognozowaniu efektywności pracy wypożyczalni rowerowych
Application of fuzzy cognitive map to predict of effectiveness of bike sharing systems
Autorzy:
Jastriebow, A.
Kubuś, Ł.
Poczęta, K.
Powiązania:
https://bibliotekanauki.pl/articles/408030.pdf
Data publikacji:
2017
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
rozmyta mapa kognitywna
model prognozowania
obliczenia ewolucyjne
uczenie maszynowe
fuzzy cognitive map
predictive model
evolutionary computation
machine learning
Opis:
W pracy zaproponowano zastosowanie rozmytej mapy kognitywnej wraz z ewolucyjnymi algorytmami uczenia do modelowania systemu prognozowania efektywności pracy wypożyczalni rowerowych. Na podstawie danych historycznych zbudowano rozmytą mapę kognitywną, którą następnie zastosowano do prognozowania liczby rowerzystów i klientów wypożyczalni w trzech kolejnych dniach. Proces uczenia zrealizowano z zastosowaniem indywidualnego kierunkowego algorytmu ewolucyjnego IDEA oraz algorytmu genetycznego z kodowaniem zmiennoprzecinkowym RCGA. Analizę symulacyjną systemu prognozowania efektywności pracy wypożyczalni rowerowych przeprowadzono przy pomocy oprogramowania opracowanego w technologii JAVA.
This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2017, 7, 4; 70-73
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
Autorzy:
Guillou, N.
Chapalain, G.
Powiązania:
https://bibliotekanauki.pl/articles/2078822.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
multiple regression model
artificial neural network
multilayer perceptron
regression function
machine learning algorithm
sea level
Opis:
Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.
Źródło:
Oceanologia; 2021, 63, 4; 531-544
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 25

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