Netherlands Red Cross
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  • Netherlands Red Cross
    Updated June 8, 2017 | Dataset date: Jun 1, 2017
    Product This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization An example map is available here: http://bit.ly/SriLankaFloodMap Linked data Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.
  • Netherlands Red Cross
    Updated May 19, 2017 | Dataset date: Nov 22, 2016
    Blog post about this prediction can be found here: http://bit.ly/2fWF2jq 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 learn.py 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 510.global the data innovation initiative of the Netherlands Red Cross.
    • XLSX
    • CSV
    • This dataset updates: Never
  • Netherlands Red Cross
    Updated March 29, 2017 | Dataset date: Oct 13, 2016
    Accumulated precipitation for Haiti - Hurricane Matthew - October 3-6th 2016 Based on GPM IMERG data. See comments for assumptions and workflow.
  • Please note that the windspeed and track dataset only covers the part where the this was still a tropical cyclone. For explanation see below caveats. Due to this we will not release a priority index, since windspeed data is missing for most of the country. Dataset of windspeed and track was kindly provided by University College London. The rainfall data is calculated based on GPM. It is the accumulated rainfall from March 6th midnight to March 10th 10:00am Madagascar time.
  • Netherlands Red Cross
    Updated February 18, 2017 | Dataset date: Feb 17, 2017
    Dataset contains windspeeds in miles per hour.
  • Netherlands Red Cross
    Updated February 7, 2017 | Dataset date: Dec 26, 2016
    This dataset contains: windspeeds of Typhoon Nina rainfall of Typhoon Nina Priority Index of Typhoon Nina The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, 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 houses damaged and completely destroyed. The output is 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 absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Scoring The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899 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 (mph) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) Roof and wall materials New geographical features 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 510.global the data innovation initiative of the Netherlands Red Cross.
  • Netherlands Red Cross
    Updated November 15, 2016 | Dataset date: Oct 24, 2016
    NDRRMC report of October 28th 2016
    • CSV
    • XLSX
    • This dataset updates: Never
  • Counts of damage and casualties from official data sets
  • This data is created using GPM data. It contains the accumulated precipitation for the periods October 11-18th, October 18-20th and the total over both periods.
  • Netherlands Red Cross
    Updated October 28, 2016 | Dataset date: Jan 10, 2015
    Flood extend data of Malawi floods in January 2015
  • Netherlands Red Cross
    Updated October 26, 2016 | Dataset date: Oct 20, 2016
    Data provided by Mark Saunders at University College London