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Tytuł:
A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
Autorzy:
Žigman, D.
Meštrović, K.
Tomiša, T.
Powiązania:
https://bibliotekanauki.pl/articles/2172468.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
wind turbine
small wind turbine
decision tree model
artificial neural network model
random forest model
micro-grids
spatially constrained remote Island micro-grids
remote Island micro-grid
Opis:
Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Attribute selection for stroke prediction
Autorzy:
Zdrodowska, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/386466.pdf
Data publikacji:
2019
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
data mining
classifier
J48 (C4.5)
CART
PART
naive Bayes classifier
random forest
support vector machine
multilayer perceptron
haemorrhagic stroke
ischemic stroke
Opis:
Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.
Źródło:
Acta Mechanica et Automatica; 2019, 13, 3; 200-204
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessing the efficiency of a random forest regression model for estimating water quality indicators
Autorzy:
Zavareh, Maryam
Maggioni, Viviana
Zhang, Xinxuan
Powiązania:
https://bibliotekanauki.pl/articles/27810498.pdf
Data publikacji:
2023
Wydawca:
Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:
Random Forest
water quality
hydrometeorological information
Opis:
This work evaluates the efficiency of Random Forest (RF) regression for predicting water quality indicators and investigates factors affecting water quality in 11 watersheds in Virginia, District of Columbia, and Maryland. Ten years of daily water quality data along with hydro-meteorological information (such as precipitation) and watershed physiology and characteristics (e.g., size, soil type, land use) are used to predict dissolved oxygen (DO), specific conductivity (K), and turbidity (Tu) across the selected watersheds. The RF regression model is developed for six scenarios, with an increasing number of predictors introduced in each scenario. The first scenario contains the smallest amount of information (water quality indicators DO, K and Tu), while scenario 6 contains all the available variables. The RF model is evaluated based on three statistical metrics: the relative root mean square error, the correlation coefficient, and the percentage of variance explained. In addition, the degree of importance for each predictor is used to rank their importance within each scenario. The model shows excellent performance for DO as the predicted variable. The model predicting K slightly outperforms the one predicting Tu. Scenario 4 (built based on water quality indicators, hydro-meteorological data, watershed physiology and land cover information) provided the best tradeoff between performance and efficiency (quantified in terms of the amount of information needed to develop the model). In conclusion, based on the RF model, land cover plays a significant role in predicting water quality indicators. In addition, the developed RF regression model is adaptable to watersheds in this region over a range of climates.
Źródło:
Meteorology Hydrology and Water Management. Research and Operational Applications; 2023, 11, 2; 1--18
2299-3835
2353-5652
Pojawia się w:
Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan
Autorzy:
Yaseen, Muhammad Waseem
Awais, Muhammad
Riaz, Khuram
Rasheed, Muhammad Babar
Waqar, Muhammad
Rasheed, Sajid
Powiązania:
https://bibliotekanauki.pl/articles/31340346.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:
hydrometeorology
random forest
support vector
multilayer perceptron
machine learning
flood forecasting
Opis:
Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
Źródło:
Archives of Hydro-Engineering and Environmental Mechanics; 2022, 69, 1; 59-77
1231-3726
Pojawia się w:
Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Semantic Segmentation of Diseases in Mushrooms using Enhanced Random Forest
Autorzy:
Yacharam, Rakesh Kumar
Sekhar, Dr. V. Chandra
Powiązania:
https://bibliotekanauki.pl/articles/31339414.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
mushroom diseases
semantic segmentation
computer aided
Machine Learning
significant feature extraction
Random Forest classifier
Opis:
Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.
Źródło:
Machine Graphics & Vision; 2023, 32, 2; 129-146
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Data-mining Technique for Seismic Vulnerability Assessment
Autorzy:
Wojcik, Waldemar
Karmenova, Markhaba
Smailova, Saule
Tlebaldinova, Aizhan
Belbeubaev, Alisher
Powiązania:
https://bibliotekanauki.pl/articles/1844631.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
data analysis
seismic assessment
clustering
h-means
k-means
random forest
Opis:
Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today’s machine learning methods allow the use of “fast” methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 2; 261-266
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection
Autorzy:
Wasilewska, Małgorzata
Bogucka, Hanna
Powiązania:
https://bibliotekanauki.pl/articles/226216.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
spectrum sensing
cognitive radio
machine learning
energy detection
4G
LTE
5G
k-nearest neighbors
random forest
Opis:
In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbor sand Random Forest show that these methods significantly improves the detection probability.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 1; 217-223
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble-based Method of Fraud Detection at Self-checkouts in Retail
Autorzy:
Vitynskyi, P.
Tkachenko, R.
Izonin, I.
Powiązania:
https://bibliotekanauki.pl/articles/410756.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
classification
Ensemble-based method
Random Forest
fraud detection
retail
Ito decomposition
imbalanced dataset
Opis:
The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2019, 8, 2; 3-8
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring Vegetation Cover Changes by Sentinel-1 Radar Images Using Random Forest Classification Method
Autorzy:
Tran, Van Anh
Le, Thi Le
Nguyen, Nhu Hung
Le, Thanh Nghi
Tran, Hong Hanh
Powiązania:
https://bibliotekanauki.pl/articles/2020227.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
vegetation cover change,
Sentinel-1
Random Forest
Binh Duong
Vietnam
Wietnam
wegetacja
Opis:
Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forests account for more than 40% of the total land area and have a very rich and diverse vegetation. Monitoring the changes in the vegetation cover is obviously important yet challenging, considering such large varying areas and climatic conditions. A traditional remote sensing technique to monitor the vegetation cover involves the use of optical satellite images. However, in presence of the cloud cover, the analyses done using optical satellite image are not reliable. In such a scenario, radar images are a useful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day or night. In this study, we have used multi temporal C band satellite images to monitor vegetation cover changes for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta, Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of five land cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases, using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conduct Random Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the model with nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappa of 0.97. The results demonstrated that using VH polarization, Sentinel-1 gives quite a good accuracy for vegetation cover change. Therefore, Sentinel-1 can also be used to generate reliable land cover maps suitable for different applications.
Źródło:
Inżynieria Mineralna; 2021, 2; 441--451
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of machine learning algorithms to predict permeability in tight sandstone formations
Zastosowanie metod uczenia maszynowego do przewidywania przepuszczalności w formacjach zwięzłych piaskowców typu tight gas
Autorzy:
Topór, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2143653.pdf
Data publikacji:
2021
Wydawca:
Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:
machine learning
random forest
permeability
prediction
uczenie maszynowe
lasy losowe
predykcja
przepuszczalność
Opis:
The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.
Zastosowanie algorytmów uczenia maszynowego w geologii naftowej otworzyło nowy rozdział w poszukiwaniu złóż ropy i gazu. Algorytmy uczenia maszynowego zostały z powodzeniem wykorzystane do przewidywania kluczowych właściwości petrofizycznych charakteryzujących złoże. W pracy zastosowano metody uczenia maszynowego do przewidywania przepuszczalności w warunkach ustalonego ciśnienia złożowego dla formacji zwięzłych piaskowców typu tight gas. Modele zostały skonstruowane przy użyciu algorytmów o różnym stopniu komplikacji (wielowymiarowa regresja liniowa – MLR i lasy losowe – RF), a następnie poddano je procesowi uczenia na danych zawierających podstawowe informacje o otworze, podstawowe parametry petrofizyczne oraz typ skał pochodzący z makroskopowego i mikroskopowego opisu próbek rdzeni. Typ skał został rozkodowany i poddany procesowi inżynierii cech, aby wydobyć dodatkowe zmienne do modelu. Proces uczenia na zbiorze treningowym został przeprowadzony z wykorzystaniem 10-krotnej kroswalidacji. Uzyskane wyniki pokazują, że oba algorytmy mogą przewidywać przepuszczalność z dużą dokładnością (R2 = 0,800 dla MLR vs R2 = 0,834 dla RF). Dokładność modelu RF jest około 3% lepsza niż MLR i około 6% lepsza w porównaniu do modelu referencyjnego (model regresji liniowej z jedną zmienną – porowatością). W przypadku obu modeli porowatość była najistotniejszym parametrem przy przewidywaniu przepuszczalności. Dodatkowo w modelu wykorzystującym lasy losowe istotną cechą okazała się głębokość próbki, co może świadczyć o dodatkowych interakcjach pomiędzy zmiennymi. Cechą wspólną próbek w zbiorze treningowym i testowym, dla których modele zadziałały ze słabą skutecznością, były porowatość od 3% do 10% i brak spękań. Dodatkowo w zbiorze testowym niska dokładność przewidywań przepuszczalności była związana z obecnością cementacji kalcytem i kwarcem. Workflow wykorzystujący stan wiedzy dotyczącej modelowania, którego trzon stanowi pakiet tidymodels, będzie dalej stosowany do prognozowania przestrzennych właściwości petrofizycznych na podstawie atrybutów sejsmicznych.
Źródło:
Nafta-Gaz; 2021, 77, 5; 283-292
0867-8871
Pojawia się w:
Nafta-Gaz
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of machine learning tools for seismic reservoir characterization study of porosity and saturation type
Zastosowanie metod uczenia maszynowego do charakterystyki porowatości i typu nasycenia przy użyciu atrybutów sejsmicznych
Autorzy:
Topór, Tomasz
Sowiżdżał, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/2143329.pdf
Data publikacji:
2022
Wydawca:
Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:
machine learning
random forest
XGBoost
seismic attributes
reservoir properties prediction
uczenie maszynowe
lasy losowe
drzewa wzmocnione gradientowo
atrybuty sejsmiczne
predykcja własności zbiornikowych
Opis:
The application of machine learning (ML) tools and data-driven modeling became a standard approach for solving many problems in exploration geology and contributed to the discovery of new reservoirs. This study explores an application of machine learning ensemble methods – random forest (RF) and extreme gradient boosting (XGBoost) to derive porosity and saturation type (gas/water) in multihorizon sandstone formations from Miocene deposits of the Carpathian Foredeep. The training of ML algorithms was divided into two stages. First, the RF algorithm was used to compute porosity based on seismic attributes and well location coordinates. The obtained results were used as an extra feature to saturation type modeling using the XGBoost algorithm. The XGBoost was run with and without well location coordinates to evaluate the influence of the spatial information for the modeling performance. The hyperparameters for each model were tuned using the Bayesian optimization algorithm. To check the training models' robustness, 10-fold cross-validation was performed. The results were evaluated using standard metrics, for regression and classification, on training and testing sets. The residual mean standard error (RMSE) for porosity prediction with RF for training and testing was close to 0.053, providing no evidence of overfitting. Feature importance analysis revealed that the most influential variables for porosity prediction were spatial coordinates and seismic attributes sweetness. The results of XGBoost modeling (variant 1) demonstrated that the algorithm could accurately predict saturation type despite the class imbalance issue. The sensitivity for XGBoost on training and testing data was high and equaled 0.862 and 0.920, respectively. The XGBoost model relied on computed porosity and spatial coordinates. The obtained sensitivity results for both training and testing sets dropped significantly by about 10% when well location coordinates were removed (variant 2). In this case, the three most influential features were computed porosity, seismic amplitude contrast, and iso-frequency component (15 Hz) attribute. The obtained results were imported to Petrel software to present the spatial distribution of porosity and saturation type. The latter parameter was given with probability distribution, which allows for identifying potential target zones enriched in gas.
Metody uczenia maszynowego stanowią obecnie rutynowe narzędzie wykorzystywane przy rozwiązywaniu wielu problemów w geologii poszukiwawczej i przyczyniają się do odkrycia nowych złóż. Prezentowana praca pokazuje zastosowanie dwóch algorytmów uczenia maszynowego – lasów losowych (RF) i drzew wzmocnionych gradientowo (XGBoost) do wyznaczenia porowatości i typu nasycenia (gaz/woda) w formacjach piaskowców będących potencjalnymi horyzontami gazonośnymi w mioceńskich osadach zapadliska przedkarpackiego. Proces uczenia maszynowego został podzielony na dwa etapy. W pierwszym etapie użyto RF do obliczenia porowatości na podstawie danych pochodzących z atrybutów sejsmicznych oraz współrzędnych lokalizacji otworów. Uzyskane wyniki zostały wykorzystane jako dodatkowa cecha przy modelowaniu typu nasycenia z zastosowaniem algorytmu XGBoost. Modelowanie za pomocą XGBoost został przeprowadzone w dwóch wariantach – z wykorzystaniem lokalizacji otworów oraz bez nich w celu oceny wpływu informacji przestrzennych na wydajność modelowania. Proces strojenia hiperparametrów dla poszczególnych modeli został przeprowadzony z wykorzystaniem optymalizacji Bayesa. Wyniki procesu modelowania zostały ocenione na zbiorach treningowym i testowym przy użyciu standardowych metryk wykorzystywanych do rozwiązywania problemów regresyjnych i klasyfikacyjnych. Dodatkowo, aby wzmocnić wiarygodność modeli treningowych, przeprowadzona została 10-krotna kroswalidacja. Pierwiastek błędu średniokwadratowego (RMSE) dla wymodelowanej porowatości na zbiorach treningowym i testowym był bliski 0,053 co wskazuje na brak nadmiernego dopasowania modelu (ang. overfitting). Analiza istotności cech ujawniła, że zmienną najbardziej wpływającą na prognozowanie porowatości były współrzędne lokalizacji otworów oraz atrybut sejsmiczny sweetness. Wyniki modelowania XGBoost (wariant 1) wykazały, że algorytm jest w stanie dokładnie przewidywać typ nasycenia pomimo problemu z nierównowagą klas. Czułość wykrywania potencjalnych stref gazowych w przypadku modelu XGBoost była wysoka zarówno dla zbioru treningowego, jak i testowego (0,862 i 0,920). W swoich predykcjach model opierał się głównie na wyliczonej porowatości oraz współrzędnych otworów. Czułość dla uzyskanych wyników na zbiorze treningowym i testowym spadła o około 10%, gdy usunięto współrzędne lokalizacji otworów (wariant 2 XGBoost). W tym przypadku trzema najważniejszymi cechami były obliczona porowatość oraz atrybut sejsmiczny amplitude contrast i atrybut iso-frequency component (15 Hz). Uzyskane wyniki zostały zaimportowane do programu Petrel, aby przedstawić przestrzenny rozkład porowatości i typu nasycenia. Ten ostatni parametr został przedstawiony wraz z rozkładem prawdopodobieństwa, co dało wgląd w strefy o najwyższym potencjale gazowym.
Źródło:
Nafta-Gaz; 2022, 78, 3; 165-175
0867-8871
Pojawia się w:
Nafta-Gaz
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees
Autorzy:
Tambouratzis, T>
Souliou, D.
Chalikias, M.
Gregoriades, A.
Powiązania:
https://bibliotekanauki.pl/articles/91652.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
traffic accident
location
prediction
probabilistic neural networks
random forest
accuracy
efficiency
decision tree
Opis:
The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 31-42
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/2055780.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
multi-source image fusion
random forest
support vector machine
DEM extraction
unplanned unsafe areas
remote sensing
Opis:
Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/1837996.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
machine learning
random forest
maximum likelihood
support vector machines
backpropagation
image classification
Opis:
The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting the stability of open stopes using Machine Learning
Autorzy:
Szmigiel, Alicja
Apel, Derek B.
Powiązania:
https://bibliotekanauki.pl/articles/2201415.pdf
Data publikacji:
2022
Wydawca:
Główny Instytut Górnictwa
Tematy:
open stope
machine learning
logistic regression
random forest
system otwartych komór
uczenie maszynowe
regresja logistyczna
las losowy
Opis:
The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.
Źródło:
Journal of Sustainable Mining; 2022, 21, 3; 241--248
2300-1364
2300-3960
Pojawia się w:
Journal of Sustainable Mining
Dostawca treści:
Biblioteka Nauki
Artykuł
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