Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: parcianka

Sortuj według:

Ogranicz wyniki do:

Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

Khosravi Khabat, Golkarian Ali, Omidvar Ebrahim, Hatamiafkoueieh Javad, Shirali Masoud

Acta Geophysica

2023

Vol. 71, no. 2

1015--1031

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutclife efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r=0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE=0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE=0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe.

Regression Model for the Bagging Fatigue of Knitted Fabrics Produced from Viscose/Polyester Blended Rotor Yarns

Hasani H., Hassan Zadeh S.

Fibres & Textiles in Eastern Europe

2012

Vol. 20, no. 4 (93)

67--71

The aim of this work was to predict the bagging fatigue percentage of knitted fabrics produced from viscose/polyester blended rotor yarns using blend ratios and structural cell stitch lengths as predictor variables. A simplex lattice design was used to determine the combinations of blend ratios of the fibre types. Knitted fabrics with three different structures were produced from viscose/polyester blended rotor yarns. Mixture-process crossed regression models with two mixture components and one process variable (structural cell stitch lengths, blend ratio) were built to predict the bagging fatigue percentage. All statistical analysis steps were implemented using Design-Expert statistical software. The correlation coefficient between the bagging fatigue percentage predicted and the bagging fatigue percentage observed was 0.983, indicating the strong predictive capability of the regression model built.

Badano wypychanie dzianin wykonanych z wiskozowo-poliestrowych mieszankowych przędz rotorowych. Jako wielkości wejściowe przyjęto procentowy udział włókien w mieszankach splotu. Zastosowano konstrukcję sympleksu dla określenia kombinacji stosunku składników przędzy mieszankowej. Wyprodukowano trzy rodzaje dzianin przy użyciu rożnych mieszanek przędz, utworzono model regresji zawierający dwa składniki mieszanki i jedną zmienną procesu - długość splotu dziewiarskiego. Przeprowadzono analizę statystyczną przy wykorzystaniu programu Design-Expert. Uzyskano bardzo dobrą zgodność pomiędzy wartościami przewidywanymi i pomierzonymi, współczynnik korelacji wynosił 0.983.