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Air Pollution Forecasting with Machine Learning by Using WRF-Chem Model Output

Air Pollution Forecasting with Machine Learning by Using WRF-Chem Model Output. Umur Dinç¹, Zeynep Feriha Ünal¹, Hüseyin Toros¹ ¹Meteorological Engineering Department, Istanbul Technical University, Istanbul. Content. What is Machine Learning? General Information about H20 Model

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Air Pollution Forecasting with Machine Learning by Using WRF-Chem Model Output

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  1. Air Pollution Forecasting with Machine Learning by Using WRF-Chem Model Output Umur Dinç¹, Zeynep Feriha Ünal¹, Hüseyin Toros¹ ¹Meteorological Engineering Department, Istanbul Technical University, Istanbul

  2. Content • What is Machine Learning? • General Information about H20 Model • *Usage areas,main principles • Model Configurations • *WRF-Chem Model • *H20 Model • Results • *WRF-Chem/Observed Data & H20 Model/Observed Data • Conclusion and Future Work

  3. What is Machine Learning? • Machine learning is an popular methodforpredictionnowadays. • Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. (Varone et al.,n.d) https://www.psychologytoday.com/us/blog/the-future-brain/201801/the-unbearable-conundrum-ai-consciousness

  4. What is Machine Learning? https://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-vs-machine-learning-vs-deep-learning

  5. General Information about H20 Model • There are many machining learning approaches which are widely used by big communities such as TensorFlow, Pytorch. We choose H2O for our study The reason why we selected as our study model, H2O has an easy user interface to perform great predictional applications. H2O is an open-source machine learning platform which is one of the most common used machine learning platforms. • Model is trainedwithrelativedatasetswhichwewanttopredict. Weneedto put allvariableswithourtarget data toteachour model. • Usage Areas: Finance,Health Services, Marketing,Telecom etc.

  6. General Information about H20 Model H20 Model is thatweused a packagelocated in R software. Therearepackages in other software as well. H20 Architecture (Candel,A. 2014)

  7. StudyArea Dilovası OSB-1 Station is located in Gebze which is an areabetween Kocaeli andIstanbul. Dilovası OSB-1 Station’s PM10 data is usedfortrainourgbm model. Dilovası region is one of themostpollutedarea in Turkey. Weareusing WRF-Chem as a toolfor a solution of airpollution.

  8. Model Configurations for WRF-Chem One domain (10kmx10km resolution) is used for WRF-Chem. WRF-3.9.1.1 versionusedwith GDAS data. 30 Verticallevels is used. HTAP 2010 data is used with Anthro_emisspreprocesser for Anthropogenic Emissions. Werun WRF-Chemhourlyfor 6 monthbetween 1 January 2018 – 1 July 2018. 100x100 gridpointsareused.

  9. Model Configurations for WRF-Chem &physics physics_suite = 'CONUS' radt = 30, 30, 30, bldt = 0, 0, 0, cudt = 5, 5, 5, icloud = 1, num_soil_layers = 4, num_land_cat = 21, sf_urban_physics = 1, 0, 0, sf_surface_physics = 2, 2, 2, /

  10. Model Configurations for WRF-Chem Chemical options &chem kemit = 8, chem_opt = 112, photdt = 30, chemdt = 2.0, io_style_emissions = 2, emiss_opt = 5, 0, emiss_inpt_opt = 1, 1, chem_in_opt = 1, 0, depo_fact = 0.25, gas_bc_opt = 1, 0, gas_ic_opt = 1, 0, aer_bc_opt = 1, 0, aer_ic_opt = 1, 0, gaschem_onoff = 1, 0, aerchem_onoff = 1, 0, vertmix_onoff = 1, 0,

  11. How didweuse H2O Werunthe WRF-Chem Model for 6 month. Afterthat, weused 5.5 monthhourlywrf-chem model output data totrain H20 model in R software withgradientboostingmachinemethod. Relativehumidity, temperature, Pressure, PM10, WindSpeedandcompenents, incomingshortwaveradiation, outgoinglongwaveradiationareusedfortraining. Aftertraining, Weused 15 days’ data to test ourmachinelearning model.

  12. Results

  13. Results

  14. Results H20 Model’sVariableImportanceRank

  15. Conclusion and Future Work We can seethat Machine learning model increasedaccuracysignificantly. Forthebetterresults, weneedbetteremissioninventoryfor WRF-Chem model. Wewanttousemachinelearningtoimproveour model results.

  16. Thank you for listening..

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