MARC ayrıntıları
| 000 -BAŞLIK |
|---|
| Sabit Uzunluktaki Kontrol Alanı |
03336nam a22002897a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ |
|---|
| Kontrol Alanı |
KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI |
|---|
| Kontrol Alanı |
20230713095529.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ |
|---|
| Sabit Alan |
230713d2023 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI |
|---|
| Özgün Kataloglama Kurumu |
CY-NiCIU |
| Kataloglama Dili |
eng |
| Çeviri Kurumu |
CY-NiCIU |
| Açıklama Kuralları |
rda |
| 041 ## - DİL KODU |
|---|
| Metin ya da ses kaydının dil kodu |
eng |
| 090 ## - Yerel Tasnif No |
|---|
| tasnif no |
YL 2896 |
| Cutter no |
A73 2023 |
| 100 1# - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Araromi, Olaoluwa Shadrack |
| 245 10 - ESER ADI BİLDİRİMİ |
|---|
| Başlık |
COMPARATIVE ASSESSMENT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR NO2 AND SO2 CONCENTRATION PREDICTION / |
| Sorumluluk Bildirimi |
OLAOLUWA SHADRACK ARAROMI; SUPERVISOR: ASSOC. PROF. DR. SEDEF ÇAKIR |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 300 ## - FİZİKSEL TANIMLAMA |
|---|
| Sayfa, Cilt vb. |
xi, 88 sheets; |
| Boyutları |
31 cm. |
| Birlikteki Materyal |
Includes CD |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| Media type code |
n |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| Carrier type code |
nc |
| 502 ## - TEZ NOTU |
|---|
| Tez Notu |
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Environmental Sciences Department |
| 504 ## - BİBLİYOGRAFİ NOTU |
|---|
| Bibliyografi Notu |
Includes bibliography (sheets 69-82) |
| 520 ## - ÖZET NOTU |
|---|
| Özet notu |
ABSTRACT<br/>Air pollution is a significant environmental concern due to its negative impacts on <br/>human health and the environment. Accurate prediction of NO2 and SO2 levels is <br/>important for air quality management efforts, as it allows authorities to take <br/>preventative measures to mitigate potentially hazardous conditions. In this study, <br/>machine-learning techniques were used to predict hourly concentration of NO2 and <br/>daily concentration of SO2 pollutant levels using a range of input variables including <br/>wind speed, temperature, pressure, relative humidity, hour, day, year, and month. Data <br/>was used between 2012 and 2015, and 15 different models were constructed using <br/>various combinations of input variables for both pollutant considered. The algorithms <br/>utilized were extreme gradient boost, light gradient boost, random forest, support <br/>vector regression. The machine learning models were compared with the multiple <br/>linear regression. Seasonal predictions were also made using these models, and the <br/>results showed that the combination of input variables and model used significantly <br/>influenced the accuracy of the predictions. The models with the lowest root mean <br/>squared error (RMSE) and mean squared error (MSE) values were the Light GBM and <br/>XGBoost models, with the Light GBM algorithm showing the best prediction with an <br/>R-value of 0.778 for model B, which included 7 input variables. The results also <br/>showed that the inclusion of the pressure variable in model A (8 input variables) <br/>reduced the prediction accuracy for the XGBoost model. The random forest model <br/>provided the most accurate predictions overall, particularly for the fall season. These <br/>findings demonstrate the importance of both the number and type of input variables in <br/>predicting NO2 levels and the potential for machine learning to support air quality <br/>management efforts. It is also seen in the result that the XGBoost showed the most <br/>accurate prediction for the seasons considered, except from summer, where the random <br/>forest algorithm gave a better accuracy for SO2 prediction<br/>Key words: Machine learning, NO2, Prediction, Random Forest, Root Mean Square,<br/>Seasonal, SO2 |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Machine learning |
| Alt başlık biçimi |
Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Nitrogen dioxide |
| Alt başlık biçimi |
Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Sulfur dioxide |
| Alt başlık biçimi |
Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Çakır, Sedef |
| İlişkili Terim |
supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) |
|---|
| Sınıflama Kaynağı |
Dewey Onlu Sınıflama Sistemi |
| Materyal Türü |
Thesis |