Temat: environmental learning - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Effective Pro-Environmental Education as an Important Element of Economic Infrastructure and Sustainable Development
Autorzy:: Kostecka, Joanna
Podolak, Agnieszka
Mazur-Pączka, Anna
Garczyńska, Mariola
Pączka, Grzegorz
Powiązania:: https://bibliotekanauki.pl/articles/2202233.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: pro-environmental education
active learning
survey
evaluation
Opis:: The study presents the “EREJ” project tested with students at the University of Rzeszów. The project is important for the development of the ability to search for materials for work. It improves the ability to prepare a PPT presentation and write an essay, skills in oral and written communication, the ability to work individually and in a group, the ability to discuss, transmit feedback, and present work publically. The project is also important for increasing the knowledge in the field of environmental issues.
Źródło:: Journal of Ecological Engineering; 2022, 23, 11; 132--138
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Soil Salinity Classification Using Machine Learning Algorithms and Radar Data in the Case from the South of Kazakhstan
Autorzy:: Merembayev, Timur
Amirgaliyev, Yedilkhan
Saurov, Sultan
Wójcik, Waldemar
Powiązania:: https://bibliotekanauki.pl/articles/2202157.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: environmental correlation
soil salinity
machine learning
remote sensing
Opis:: Soil salinity is one of the major impact factors on agriculture in the South of Kazakhstan. Prediction and estimation of soil salinity before planting a season usually helps to plan for the leaching of the salt. In the paper, satellite data such as radar data and machine learning algorithms, were used to classify soil salinity. Numerical results were presented for the Turkestan region, which contains more than 102 points. The machine learning algorithms, including Gaussian Process, Decision Tree, and Random Forest, were compared. The evaluation of the model score was realized by using metrics, such as accuracy, Recall, and f1. In addition, the influence of the dataset features on the classification was investigated using machine learning algorithms. The research results showed that the Gaussian Process model has the best score among considered algorithms. In addition, the results are consistent with the outcome of the Shapley Additive exPlanations (SHAP) framework.
Źródło:: Journal of Ecological Engineering; 2022, 23, 10; 61--67
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River
Autorzy:: Sillberg, Chalisa Veesommai
Kullavanijaya, Pratin
Chavalparit, Orathai
Powiązania:: https://bibliotekanauki.pl/articles/1955579.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: environmental data analysis
machine learning
SVM
support vector machine
water quality index
WQI
Opis:: The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River’s water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH3-N), nitrate (NO3-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH3-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water’s quality with 0.86-0.95 accuracy when applied three to six attributes.
Źródło:: Journal of Ecological Engineering; 2021, 22, 9; 70-86
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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