abdousadou / kdd2018_air_pollution_prediction Goto Github PK

KDD 2018 held a competition to predict the intensity of air pollutants in Beijing and London for the next 48 hours during a month. This is our team's code (NIPL Rises) that achieved 16th position (among 4000 teams) in last 10 days category executed on a single laptop. We used tensorflow to build a hybrid model composed of CNNs, LSTMs, and MLPs for an end-to-end prediction over 2D grid data, time-series, and categorical data. This project includes (1) fetching and crawling of weather and pollutant data from multiple sources, (2) data cleaning and integration, (3) visualization for insights, and (4) prediction of pollutants (PM2.5, PM10, O3) for the next 48 hours in Beijing and London.

1. Download csv files from KDD 2018 data repository (requires sign-up),
2. Install required packages including tensorflow and keras (for deep learning), selenium (for web crawling),
3. Copy default.config.ini to config.ini,
4. Download chromedriver.exe for web crawling, and set the address

   CHROME_DRIVER_PATH = path to chromedriver.exe
   

5. Set the addresses of downloaded data-sets,

   BJ_AQ = Beijing air quality history
   BJ_AQ_REST = Beijing air quality history for 2nd and 3rd months of 2018
   BJ_AQ_STATIONS = Beijing air quality stations
   BJ_MEO = Beijing meteorological history
   BJ_GRID_DATA = Beijing grid weather history
   LD_* = same for London
   

6. Set the addresses for fetched/cleaned data to be stored,

   BJ_AQ_LIVE = fetched Beijing air quality live data
   BJ_MEO_LIVE = fetched Beijing meteorology live data
   BJ_OBSERVED = cleaned Beijing observed air quality and meteorology time series
   BJ_OBSERVED_MISS = marked missing data in BJ_OBSERVED
   BJ_STATIONS = cleaned data of stations in Beijing
   BJ_GRID_LIVE = fetched grid of current weather in Beijing
   BJ_GRID_FORECAST = fetched grid of forecast weather in Beijing
   BJ_GRIDS = history of grid data in Beijing
   BJ_GRID_COARSE = coarsened grid of data to lower resolutions
   LD_* = same for London
   

7. Set [lower, upper] bounds for date intervals of urls

   BJ_AQ_URL = */2018-06-05-0/2k0d1d8
   BJ_MEO_URL = */2018-06-05-0/2k0d1d8
   BJ_GRID_URL = */2018-06-05-0/2k0d1d8
   LD_*_URL = same for London
   

8. Set a path for generated features and models

   FEATURE_DIR = directory for extracted features
   MODEL_DIR = directory for generated models
   

1. Data pre-process
   1. Run src/preprocess/preprocess_all.py to create the cleaned data sets in your pre-specified addresses,
2. Data visualization
   1. Run scripts in src/statistics to gain basic insights about value distributions, time series and geographical positions
   2. Change BJ_* to LD_* for London data
3. Feature generation
   1. Go to main method of src/feature_generators/hybrid_fg.py,
   2. Uncomment desired (city-pollutant, sample rate) pairs in cases variable (all pairs are eventually required). Higher sample rate, larger data,
   3. Run the script
4. Model training
   1. Go to src/methods/lstm_pre_train.py
   2. Run the script; simple LSTM models are pre-trained for all pollutants. These models are fed (unchanged) to the final model for better performance,
   3. Go to main method of src/methods/hybrid.py,
   4. Uncomment desired city-pollutant,
   5. Run the script; best model so far will be saved automatically
5. Model testing
   1. Go to src/methods/model_tests.py,
   2. Uncomment desired city-pollutant, set a time interval in TEST_FROM and TEST_TO,
   3. Run the script; SMAPE score will be printed.
   4. Go to src/methods/model_investigate.py,
   5. Run the script to see SMAPE score per station sorted and geographically visualized.
6. Prediction
   1. Go to src/predict_next_48.py
   2. Change timedelta if you wish to predict previous 48 hours,
   3. Run the script

1. examples/gcforest includes basic examples of using forests instead of neurons to do deep learning proposed in this paper,
2. examples/tensorflow includes basic examples of using tensorflow