Abstract
Deep learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. The main purpose of this proposal is design predictor to accurately forecast air quality indices (AQIs) of the future 48 h. Accurate predictions of AQIs can bring enormous value to governments, enterprises, and the general public -and help them make informed decisions. We Will Build Model Consist of four Steps: (A) Determine the Main Rules (contractions) of avoiding emission (B) Obtaining and pre-processing reliable database from (KDD CUP 2018) (C) Building Predator have multi-level based on Long Short-term Memory network corporative with one of optimization algorithm called (Partial Swarm) to predict the PM2.5, PM10, and O3 concentration levels over the coming 48 h for every measurement station. (D) To evaluate the predictions, on each day, SMAPE scores will be calculated for each station, each hour of the day (48 h overall), and each pollutant (PM2.5, PM10, SOx, CO, O3 and NOx). The daily SMAPE score will then be the average of all the individual SMAPE scores.
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Al_Janabi, S., Yaqoob, A., Mohammad, M. (2020). Pragmatic Method Based on Intelligent Big Data Analytics to Prediction Air Pollution. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_8
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