Prediction of climatic parameters from physicochemical parameters using artificial neural networks
The knowledge of the climate of a region is a primordial task in that it allows predictions of climatic parameters in the future. In this study, monthly maximum and minimum air temperature (Tair,min, Tair,max), relative humidity (RH), and sunshine duration (SD) were modelled by multiple linear regre...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001068835/Details |
|---|---|
| Matična publikacija: |
Kemija u industriji (Online) 68 (2019), 7/8 ; str. 303-316 |
| Glavni autori: | Gheraba, Lamia (Author), Khaouane, Latifa, Benkortbi, Othmane, Hanini, Salah, Hamadache, Mabrouk |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.15255/KUI.2019.004 Kemija u industriji (Online) Hrčak |
| LEADER | 02972naa a22004334i 4500 | ||
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| 001 | NSK01001068835 | ||
| 003 | HR-ZaNSK | ||
| 005 | 20200902164258.0 | ||
| 006 | m d | ||
| 007 | cr|||||||||||| | ||
| 008 | 200709s2019 ci a |o |0|| ||eng | ||
| 024 | 7 | |2 doi |a 10.15255/KUI.2019.004 | |
| 035 | |a (HR-ZaNSK)001068835 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 041 | 0 | |a eng |b eng |b hrv | |
| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |a 66 |2 2011 | |
| 080 | 1 | |a 55 |2 2011 | |
| 100 | 1 | |a Gheraba, Lamia |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 0 | |a Prediction of climatic parameters from physicochemical parameters using artificial neural networks |h [Elektronička građa] : |b case study of Ain Defla (Algeria) / |c Lamia Gheraba, Latifa Khaouane, Othmane Benkortbi, Salah Hanini, Mabrouk Hamadache. |
| 300 | |b Ilustr. | ||
| 504 | |a Bibliografija: 30 jed. | ||
| 504 | |a Summary ; Sažetak. | ||
| 505 | 8 | |a Sadrži i: priloge na str. 312-316. | |
| 520 | |a The knowledge of the climate of a region is a primordial task in that it allows predictions of climatic parameters in the future. In this study, monthly maximum and minimum air temperature (Tair,min, Tair,max), relative humidity (RH), and sunshine duration (SD) were modelled by multiple linear regression (MLR), and multilayer perceptron methods (MLP). For the four climatic parameters, the internal and external validations of MLP-ANN model showed high R2 and Q2 values in the range 0.81–0.98. The agreement between calculated and experimental values confirmed the ability of ANN-based equation to predict these parameters quickly and at lower cost. | ||
| 520 | |a Poznavanje klime neke regije osnovni je zadatak jer omogućuje predviđanje klimatskih parametara u budućnosti. U ovom su istraživanju maksimalna i minimalna mjesečna temperatura zraka (Tair,min, Tair,max), relativna vlažnost (RH) i trajanje sunčeve svjetlosti (SD) modelirani višestrukom linearnom regresijom (MLR) i višeslojnim perceptronskim metodama (MLP). Za četiri klimatska parametra interna i eksterna validacija modela MLP-ANN pokazala je visoke vrijednosti R2 i Q2 u području 0,81 – 0,98. Usklađenost izračunatih i eksperimentalnih vrijednosti potvrdilo je da jednadžba temeljena na ANN-u brzo i uz niže troškove predviđa te parametre. | ||
| 653 | 0 | |a Modeliranje |a Neuronska mreža |a Fizikalno-kemijski parametri |a Klimatski parametri | |
| 700 | 1 | |a Khaouane, Latifa |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Benkortbi, Othmane |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hanini, Salah |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hamadache, Mabrouk |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Kemija u industriji (Online) |x 1334-9090 |g 68 (2019), 7/8 ; str. 303-316 |w nsk.(HR-ZaNSK)000530475 | |
| 981 | |b Be2019 |b B04/19 | ||
| 998 | |b tino2008 | ||
| 856 | 4 | 0 | |u https://doi.org/10.15255/KUI.2019.004 |
| 856 | 4 | 0 | |u http://silverstripe.fkit.hr/kui/issue-archive/article/673 |y Kemija u industriji (Online) |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/222488 |y Hrčak |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |