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  • 1400+ Downloads
    Updated 10 November 2019 | Dataset date: November 04, 2016-November 04, 2016
    This dataset updates: Never
    Who is doing what and where in Philippines for Typhoon Haima (Lawin)
  • 2500+ Downloads
    Updated 7 December 2016 | Dataset date: November 22, 2016-November 22, 2016
    This dataset updates: Never
    Blog post about this prediction can be found here: The predicted priority index of Typhoon Haima is produced by a machine learning algorithm that was trained on four past typhoons: Haiyan, Melor, Hagupit and Rammasun. It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on people affected and houses damaged. First run The Priority Index is a 1-5 classification that can be used to identify the worst hit areas: those that need to be visited for further assessments or support first. Second run The model now predicts two things: a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy. the precentage of total damage (damaged houses versus all houses) The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Data sources: Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX) Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX) Avg. wind speed (km/h) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) For the second run of the algorithm we also included: Roof and wall materials New geographical features The result of different models can be found in the file 'Typhoon Haima - performance of different models - second run.csv' A note on how to interpret this. date running date alg_date same alg_model name of the algorithm used alg_predict_on name of the learning variable alg_use_log i s the learning variable transformed in log code_version version of the code All the columns with feat_ indicates the importance of that feature, if not present that feature was not used. learn_matrix name of the learning matrix with the 5 typhoons run_name unique run name (pickle files and csv files have this name for this model) typhoon_to_predict name of a new typhoon to predict val_accuracy accuracy based on 10 categories of damage 0% 10% 20% … val_perc_down perc of underpredicted categories val_perc_up perc of overpredicted categories Val_best_score best r2 score Val_stdev_best_score error on best score based on the CV Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit Val_mean_error_num_houses average error on the number of houses val_median_error_num_houses median val_std_error_num_houses std deviation of the errors (lower is better) Algorithm developed by the data innovation initiative of the Netherlands Red Cross.
  • 1700+ Downloads
    Updated 15 November 2016 | Dataset date: October 24, 2016-October 24, 2016
    This dataset updates: Never
    NDRRMC report of October 28th 2016
  • 2500+ Downloads
    Updated 2 November 2016 | Dataset date: October 25, 2016-October 25, 2016
    This dataset updates: Every week
    This data is about damaged houses in the Philippines after Typhoon Haima (Lawin)
  • 1000+ Downloads
    Updated 20 October 2016 | Dataset date: October 20, 2016-October 20, 2016
    This dataset updates: Never
    Data provided by Mark Saunders at University College London