Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

Environmental Modeling & Assessment ◽

10.1007/s10666-017-9578-y ◽

2017 ◽

Vol 23 (3) ◽

pp. 229-247 ◽

Author(s):

P. J. García Nieto ◽

E. García-Gonzalo ◽

A. Bernardo Sánchez ◽

A. A. Rodríguez Miranda

Keyword(s):

Neural Network ◽

Air Quality ◽

Metropolitan Area ◽

Air Quality Modeling ◽

Northern Spain ◽

M5 Model Tree ◽

Mlp Neural Network ◽

Quality Modeling ◽

Download Full-text

Modelización y análisis de la calidad del aire en la ciudad de Oviedo (norte de España), mediante los enfoques PSO-SVM, red neuronal MLP y árbol de regresión M5 = Modeling and analysis of the air quality in the city of Oviedo (Northern Spain) using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree

10.18002/10612/7953 ◽

2018 ◽

Author(s):

Alejandro Aurelio Rodríguez Miranda

Keyword(s):

Neural Network ◽

Air Quality ◽

Northern Spain ◽

M5 Model Tree ◽

Modeling And Analysis ◽

Mlp Neural Network ◽

The City ◽

Download Full-text

Modeling pressure drop produced by different filtering media in microirrigation sand filters using the hybrid ABC-MARS-based approach, MLP neural network and M5 model tree

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2017.05.008 ◽

2017 ◽

Vol 139 ◽

pp. 65-74 ◽

Author(s):

P.J. García Nieto ◽

E. García-Gonzalo ◽

J. Bové ◽

G. Arbat ◽

M. Duran-Ros ◽

...

Keyword(s):

Neural Network ◽

Pressure Drop ◽

M5 Model Tree ◽

Sand Filters ◽

Mlp Neural Network ◽

Download Full-text

Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree

Water Resources Management ◽

10.1007/s11269-009-9535-x ◽

2009 ◽

Vol 24 (10) ◽

pp. 2007-2019 ◽

Author(s):

Krishna Kumar Singh ◽

Mahesh Pal ◽

V. P. Singh

Keyword(s):

Neural Network ◽

Backpropagation Neural Network ◽

M5 Model Tree ◽

Download Full-text

Estimation of Chemical Speciation and Temporal Allocation Factor of VOC and PM2.5 for the Weather-Air Quality Modeling in the Seoul Metropolitan Area

Journal of the Korean earth science society ◽

10.5467/jkess.2015.36.1.36 ◽

2015 ◽

Vol 36 (1) ◽

pp. 36-50

Author(s):

Yun Seob Moon ◽

Keyword(s):

Air Quality ◽

Metropolitan Area ◽

Chemical Speciation ◽

Air Quality Modeling ◽

Seoul Metropolitan Area ◽

Quality Modeling ◽

Allocation Factor

Download Full-text

Nonlinear air quality modeling using multivariate adaptive regression splines in Gijón urban area (Northern Spain) at local scale

Applied Mathematics and Computation ◽

10.1016/j.amc.2014.02.096 ◽

2014 ◽

Vol 235 ◽

pp. 50-65 ◽

Author(s):

P.J. García Nieto ◽

J.C. Álvarez Antón

Keyword(s):

Air Quality ◽

Air Quality Modeling ◽

Local Scale ◽

Multivariate Adaptive Regression Splines ◽

Regression Splines ◽

Northern Spain ◽

Quality Modeling ◽

Adaptive Regression ◽

Adaptive Regression Splines

Download Full-text

A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling

Journal of Environmental Management ◽

10.1016/j.jenvman.2012.08.020 ◽

2012 ◽

Vol 112 ◽

pp. 404-414 ◽

Author(s):

Shuiyuan Cheng ◽

Li Li ◽

Dongsheng Chen ◽

Jianbing Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Air Quality Modeling ◽

Ensemble Approach ◽

Quality Modeling ◽

Regional Air Quality

Download Full-text

Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

Journal of Climatology ◽

10.1155/2014/839205 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Author(s):

Armin Alipour ◽

Jalal Yarahmadi ◽

Maryam Mahdavi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydrological Cycle ◽

Reference Evapotranspiration ◽

Weather Data ◽

Climatic Data ◽

M5 Model Tree ◽

Artificial Neural ◽

Reference evapotranspiration (ETO) is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determine ETO using land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimate ETO values and their results were compared with calculated ETO by FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculate ETO. LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination (R2) values for comparison between calculated ETO by FAO-PM and estimated ETO by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, R2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ETO by means of LST data derived from MODIS sensor.

Download Full-text

Nonlinear Air Quality Modeling Using Support Vector Machines in Gijón Urban Area (Northern Spain) at Local Scale

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2012-0119 ◽

2013 ◽

Vol 14 (5) ◽

Author(s):

A. Suárez Sánchez ◽

P.J. García Nieto ◽

F.J. Iglesias-Rodríguez ◽

J.A. Vilán Vilán

Keyword(s):

Air Quality ◽

Support Vector Machines ◽

Air Quality Modeling ◽

Local Scale ◽

Support Vector ◽

Northern Spain ◽

Vector Machines ◽

Quality Modeling

Download Full-text

Prediction of Stability Number for Tetrapod Armour Block Using Artificial Neural Network and M5' Model Tree

Journal of Korean Society of Coastal and Ocean Engineers ◽

10.9765/kscoe.2011.23.1.109 ◽

2011 ◽

Vol 23 (1) ◽

pp. 109-117 ◽

Author(s):

Seung-Woo Kim ◽

Kyung-Duck Suh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stability Number ◽

M5 Model Tree ◽

Artificial Neural ◽

Download Full-text

Modeling algal atypical proliferation using the hybrid DE–MARS–based approach and M5 model tree in La Barca reservoir: A case study in northern Spain

Ecological Engineering ◽

10.1016/j.ecoleng.2019.02.020 ◽

2019 ◽

Vol 130 ◽

pp. 198-212 ◽

Author(s):

P.J. García-Nieto ◽

E. García-Gonzalo ◽

J.R. Alonso Fernández ◽

C. Díaz Muñiz

Keyword(s):

Northern Spain ◽

M5 Model Tree ◽

Download Full-text