Recommender system for ground-level Ozone predictions in Kuwait

This article presents a recommender system based on rough mereology for predicting Ozone concentration in Kuwait through testing the data gathered from Al-Jahra station. The proposed recommender system consists of three phases; namely pre-processing, classification, and recommendation phases. To evaluate the performance of the presented recommender system, fifteen parameters were used. Those parameters were developed and validated between Jan. 2006 and Sept. 2010. The obtained results demonstrated the effectiveness and the reliability of the proposed recommender system.

[1]  Renatus Ziegler,et al.  On the Foundations of Set Theory , 1996 .

[2]  K. N. Jallad,et al.  Analysis of ambient ozone and precursor monitoring data in a densely populated residential area of Kuwait , 2010 .

[3]  Hisham Ettouney,et al.  Analysis of ozone pollution in the Shuaiba industrial area in Kuwait , 2000 .

[4]  Hisham Ettouney,et al.  Forecasting of ozone pollution using artificial neural networks , 2009 .

[5]  J. D. Monk On the Foundations of Set Theory , 1970 .

[6]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[7]  A. Ali,et al.  Ozone monitoring instrument aerosol products: Algorithm modeling and validation with ground based measurements over Europe , 2011, The 2011 International Conference on Computer Engineering & Systems.

[8]  Basil Dimitriades,et al.  Photochemical Oxidant Formation: Overview of Current Knowledge and Emerging Issues , 1989 .

[9]  Grinding Facility,et al.  Office Of Air Quality Planning And Standards , 1976 .

[10]  Scott M. Robeson,et al.  Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations , 1990 .

[11]  B. Telenta,et al.  Application of the operational synoptic model for pollution forecasting in accidental situations , 1994 .

[12]  R L Maynard Smog Alert: Managing Urban Air Quality , 1997 .

[13]  Lech Polkowski,et al.  Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data , 2011, Int. J. Intell. Syst..

[14]  A Statistical Model for Predicting Ozone Levels , 2022 .

[15]  Eiman Tamah Al-Shammari Public warning systems for forecasting ambient ozone pollution in Kuwait , 2013, Environmental Systems Research.

[16]  H. Noordijk The National Smog Warning System In TheNetherlands; A Combination Of MeasuringAnd Modelling , 1970 .

[17]  M. F. Tolba,et al.  Data assimilation of Ozone Monitoring Instrument images for improving Aerosol Optical Depth prediction , 2012, 2012 8th International Conference on Informatics and Systems (INFOS).