Black, E., Tarnavsky, E., Maidment, R., Greatrex, H., Mookerjee, A., Quaife, T., and Brown, M.:
The use of remotely sensed rainfall for managing drought risk: A case study of weather index insurance in Zambia,
Remote Sens.-Basel,
8, 342,
https://doi.org/10.3390/rs8040342, 2016.
a,
b
Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.:
NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 5.2, Tech. rep.,
available at:
https://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V5.2_0.pdf (last access: 21 June 2020),
NASA, 2018. a
Boser, B. E., Vapnik, V. N., and Guyon, I. M.:
Training Algorithm Margin for Optimal Classifiers, COLT92: 5th Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, USA, 27–29 July 1992, 144–152, 1992. a
Bowden, G. J., Dandy, G. C., and Maier, H. R.:
Input determination for neural network models in water resources applications. Part 1 – Background and methodology,
J. Hydrol.,
301, 75–92,
https://doi.org/10.1016/j.jhydrol.2004.06.021, 2005.
a
Brakenridge, G.:
Global Active Archive of Large Flood Events,
available at:
http://floodobservatory.colorado.edu/Archives/index.html (last access: 21 June 2020), 2002.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l,
m,
n
Calvet, L., Lopeman, M., de Armas, J., Franco, G., and Juan, A. A.:
Statistical and machine learning approaches for the minimization of trigger errors in parametric earthquake catastrophe bonds,
SORT,
41, 373–391,
https://doi.org/10.2436/20.8080.02.64, 2017.
a
Chantarat, S., Mude, A. G., Barrett, C. B., and Carter, M. R.:
Designing Index-Based Livestock Insurance for Managing Asset Risk in Northern Kenya,
J. Risk Insur.,
80, 205–237,
https://doi.org/10.1111/j.1539-6975.2012.01463.x, 2013.
a
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.:
SMOTE: Synthetic Minority Over-sampling Technique,
J. Artif. Intell. Res.,
16, 321–357,
https://doi.org/10.1613/jair.953, 2002.
a
Chen, T., Ren, L., Yuan, F., Tang, T., Yang, X., Jiang, S., Liu, Y., Zhao, C., and Zhang, L.:
Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China,
Stoch. Env. Res. Risk A.,
33, 1893–1905,
https://doi.org/10.1007/s00477-019-01731-w, 2019.
a
Chiang, Y.-M., Hsu, K.-L., Chang, F.-J., Hong, Y., and Sorooshian, S.:
Merging multiple precipitation sources for flash flood forecasting,
J. Hydrol.,
340, 183–196,
https://doi.org/10.1016/j.jhydrol.2007.04.007, 2007.
a,
b
Cornell University:
CSF Caribbean Drought Atlas,
available at:
http://climatesmartfarming.org/tools/caribbean-drought/ (last access: 21 June 2020), 2018. a
CRED:
EM-DAT: The International Disaster Database,
available at:
http://www.emdat.be/database. (last access: 21 June 2020), Centre for Research on the Epidemiology of Disasters – CRED, Brussels, Belgium, 2019.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l,
m,
n,
o,
p,
q
Crone, S. F., Lessmann, S., and Stahlbock, R.:
The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing,
Eur. J. Oper. Res.,
173, 781–800,
https://doi.org/10.1016/j.ejor.2005.07.023, 2006.
a
Davies, R., Behrend, J., and Hill, E.:
FloodList,
available at:
http://floodlist.com/ (last access: 21 June 2020), Copernicus, 2008.
a,
b,
c,
d,
e,
f,
g,
h
Dreyfus, G.:
Neural Networks: Methodology and Applications, Springer Science & Business Media, 2005. a
Eckstein, D., Künzel, V., and Schäfer, L.:
Global Climate Risk Index 2018. Who Suffers Most From Extreme Weather Events? Weather-related Loss Events in 2016 and 1997 to 2016, Tech. rep.,
Germanwatch,
available at:
http://www.germanwatch.org/en/cri (last access: 21 June 2020), 2017. a
European Drought Observatory:
Standardized Precipitation Index (SPI),
available at:
https://edo.jrc.ec.europa.eu/ (last access: 21 June 2020), 2020. a
Field, C., Barros, V., Dokken, D., Mach, K., Mastrandrea, M., Bilir, T., Chatterjee, M., Ebi, K., Estrada, Y., Genova, R., Girma, B., Kissel, E., Levy, A., MacKracken, S., Mastrandrea, P., White, L., and IPCC:
Climate Change 2014: Impacts, Adaptation, and Vulnerability: Summaries, Frequently Asked Questions, and Cross-Chapter Boxes: A Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
World Meteorological Organization, Geneva, 2014. a
Figueiredo, R., Martina, M. L. V., Stephenson, D. B., and Youngman, B. D.:
A Probabilistic Paradigm for the Parametric Insurance of Natural Hazards,
Risk Anal.,
38, 2400–2414,
https://doi.org/10.1111/risa.13122, 2018.
a,
b,
c,
d,
e
Flach, P. A. and Kull, M.:
Precision-Recall-Gain curves: PR analysis done right,
Adv. Neur. In.,
2015-Januar, 838–846, 2015. a
Franco, G.:
Minimization of trigger error in cat-in-a-box parametric earthquake catastrophe bonds with an application to Costa Rica,
Earthq. Spectra,
26, 983–998,
https://doi.org/10.1193/1.3479932, 2010.
a
Fung, K. F., Huang, Y. F., Koo, C. H., and Soh, Y. W.:
Drought forecasting: A review of modelling approaches 2007–2017,
J. Water Clim. Change, 11, 771–799,
https://doi.org/10.2166/wcc.2019.236, 2019.
a,
b
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.:
The climate hazards infrared precipitation with stations–a new environmental record for monitoring extremes,
Scientific Data,
2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
a
Gagne, D., McGovern, A., Haupt, S., Sobash, R., Williams, J., and Xue, M.:
Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles,
Weather Forecast.,
32, 1819–1840,
https://doi.org/10.1175/WAF-D-17-0010.1, 2017.
a
Garcia, V., Sanchez, J. S., and Mollineda, R. A.:
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance,
Knowl.-Based Syst.,
25, 13–21,
https://doi.org/10.1016/j.knosys.2011.06.013, 2012.
a
Ghahramani, Z.:
Unsupervised learning, chap. 5,
in: Advanced Lectures on Machine Learning,
edited by: Bousquet, O., von Luxburg, U., and Ratsch, G.,
Springer-Verlag Berlin Heidelberg, Tübingen, i edn., pp. 72–112, 2004. a
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q.:
On calibration of modern neural networks,
34th International Conference on Machine Learning, ICML 2017,
3, 2130–2143, 6 August 2017–11 August 2017, International Convention Centre Sydney, Sydney, Australia, 2017. a
Hao, Z., Singh, V. P., and Xia, Y.:
Seasonal Drought Prediction: Advances, Challenges, and Future Prospects,
Rev. Geophys.,
56, 108–141,
https://doi.org/10.1002/2016RG000549, 2018.
a,
b
Herrera, D. and Ault, T. R.:
Insights from a New High-Resolution Drought Atlas for the Caribbean spanning 1959 to 2016,
B. Am. Meteorol. Soc.,
30, 7801–7825,
https://doi.org/10.1175/JCLI-D-16-0838.1, 2017.
a
Hong, Y., Hsu, K. L., Sorooshian, S., and Gao, X.:
Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system,
J. Appl. Meteorol.,
43, 1834–1852,
https://doi.org/10.1175/jam2173.1, 2004.
a
Hossin, M. and Sulaiman, M.:
A Review on Evaluation Metrics for Data Classification Evaluations,
International Journal of Data Mining & Knowledge Management Process,
5, 01–11,
https://doi.org/10.5121/ijdkp.2015.5201, 2015.
a
Huang, J., Li, Y. F., and Xie, M.:
An empirical analysis of data preprocessing for machine learning-based software cost estimation,
Inform. Softw. Tech.,
67, 108–127,
https://doi.org/10.1016/j.infsof.2015.07.004, 2015.
a
Izzo, M., Rosskopf, C. M., Aucelli, P. P. C., Maratea, A., Méndez, R., Pérez, C., and Segura, H.:
A New Climatic Map of the Dominican Republic Based on the Thornthwaite Classification,
Phys. Geogr.,
31, 455–472,
https://doi.org/10.2747/0272-3646.31.5.455, 2010.
a
Jones, K. S. and Van Rijsbergen, C. J.:
Progress in documentation: Information retrieval test collections,
J. Doc.,
32, 59–75,
https://doi.org/10.1108/eb026616, 1976.
a
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.:
CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution,
J. Hydrometeorol.,
5, 487–503,
https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
a
Khalaf, M., Hussain, A. J., Al-Jumeily, D., Baker, T., Keight, R., Lisboa, P., Fergus, P., and Al Kafri, A. S.:
A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction,
2018 IEEE Congress on Evolutionary Computation, CEC 2018 – Proceedings,
pp. 1–8, 8–13 July 2018, Rio de Janeiro, Brazil,
https://doi.org/10.1109/CEC.2018.8477904, 2018.
a
Kim, M., Park, M., Im, J., Park, S., and Lee, M.:
Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data,
Remote Sens.-Basel,
11, 1195,
https://doi.org/10.3390/rs11101195, 2019.
a
Kron, W., Löw, P., and Kundzewicz, Z. W.:
Changes in risk of extreme weather events in Europe,
Environ. Sci. Policy,
100, 74–83,
https://doi.org/10.1016/j.envsci.2019.06.007, 2019.
a
Kunreuther, H.:
Mitigation and Financial Risk Management for Natural Hazards A Risk Management Strategy for Reducing Losses and Providing Financial Protection, The Geneva Papers on Risk and Insurance. Issues and Practice,
26, 277–296, 2001. a
Ling, C. and Li, C.:
Data Mining for Direct Marketing: Problems and Solutions,
American Association for Artificial Intelligence, 73–79, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 1998. a
Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X., Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J. C., Schaepman-Strub, G., Schröder, M., and Verhoelst, T.:
Validation practices for satellite-based Earth observation data across communities,
Rev. Geophys.,
55, 779–817,
https://doi.org/10.1002/2017RG000562, 2017.
a
Maini, V. and Sabri, S.:
Machine Learning for Humans,
available at:
https://everythingcomputerscience.com/books/Machine Learning for Humans.pdf (last access: 21 June 2020), 2017. a
Makaudze, E. M. and Miranda, M. J.:
Catastrophic drought insurance based on the remotely sensed normalised difference vegetation index for smallholder farmers in Zimbabwe, Agricultural Economics Research, Policy and Practice in Southern Africa, 49, 418–432,
https://doi.org/10.1080/03031853.2010.526690, 2010.
a
McClure, N.:
TensorFlow Machine Learning,
available at:
https://www.tensorflow.org/federated (last access: 21 June 2020), 2017. a
McKee, T. B., Doesken, N. J., and Kleist, J.:
The relationship of drought frequency and duration to time scales,
in: AMS 8th Conference on Applied Climatology, January, Anaheim, California, 17–22 January 1993, available at:
https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (last access: 21 June 2020), pp. 179–184, 1993.
a,
b
Mosavi, A., Ozturk, P., and Chau, K. W.:
Flood prediction using machine learning models: Literature review,
Water (Switzerland),
10, 1536,
https://doi.org/10.3390/w10111536, 2018.
a,
b
Mosteller, F. and Tukey, J. W.:
Data analysis, including statistics,
in: Handbook of social psychology,
edited by: Lindzey, G. and Aronson, E., Addison-Wesley, 2, 80–203, 1968. a
Nayak, M. A. and Ghosh, S.:
Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier,
Theor. Appl. Climatol.,
114, 583–603,
https://doi.org/10.1007/s00704-013-0867-3, 2013.
a,
b
OCHA:
ReliefWeb,
available at:
https://reliefweb.int/ (last access: 21 June 2020), OCHA United Nations Office for the Coordination of Humanitarian Affairs, 2020.
a,
b,
c,
d,
e
Ornella, L., Kruseman, G., and Crossa, J.:
Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought,
in: Drought: Detection and Solutions,
edited by: Ondrasek, G., IntechOpen, London, UK,
https://doi.org/10.5772/57353, 2019.
a
Payano-Almanzar, R. and Rodriguez, J.:
Meteorological, Agricultural and Hydrological Drought in the Dominican Republic: A review,
Current World Environment,
13, 124–143,
https://doi.org/10.12944/CWE.13.1.12, 2018.
a,
b,
c,
d,
e
Pedregosa, F., Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., and Mueller, A.:
Scikit-learn: Machine Learning in Python,
J. Mach. Learn. Res.,
12, 2825–2830, 2011. a
Platt, J. C.:
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods,
in: Advances in Large Margin Classifiers,
edited by: Smola, A. J., Bartlett, P. L., Scholkopf, B., and Schuurmans, D.,
MIT Press, Cambridge, Massachusetts, USA, pp. 61–74, 1999. a
Richman, M. B., Leslie, L. M., and Segele, Z. T.:
Classifying Drought in Ethiopia Using Machine Learning,
Procedia Comput. Sci.,
95, 229–236,
https://doi.org/10.1016/j.procs.2016.09.319, 2016.
a
Sallis, P., Claster, W., and Hernandez, S.:
A machine-learning algorithm for wind gust prediction,
Comput. Geosci.,
37, 1337–1344,
https://doi.org/10.1016/j.cageo.2011.03.004, 2011.
a
Samuel, A. L.:
Eight-move opening utilizing generalization learning, (See Appendix B, Game G-43.1 Some Studies in Machine Learning Using the Game of Checkers),
IBM Journal,
pp. 210–229, 1959. a
Sorooshian, S., Hsu, K. L., Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D.:
Evaluation of PERSIANN system satellite-based estimates of tropical rainfall,
B. Am. Meteorol. Soc.,
81, 2035–2046,
https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2, 2000.
a
Stevens, E. and Antiga, L.:
Deep Learning with PyTorch Essential Excerpts,
Manning Publications Co., Shelter Island, New York, 2019.
a,
b,
c
Sun, Y., Wong, A. K. C., and Kamel, M. S.:
Classification of imbalanced data: A review,
Int. J. Pattern Recogn.,
23, 687–719,
https://doi.org/10.1142/S0218001409007326, 2009.
a
Surminski, S., Bouwer, L., and Linnerooth-Bayer, J.:
How insurance can support climate resilience,
Nat. Clim. Change,
6, 333–334,
https://doi.org/10.1038/nclimate2979, 2016.
a
Tate, E. L. and Gustard, A.:
Drought Definition: A Hydrological Perspective,
in: Drought and Drought Mitigation in Europe. Advances in Natural and Technological Hazards Research, edited by: Vogt, J. V. and Somma, F., vol 14., pp. 23–48,
https://doi.org/10.1007/978-94-015-9472-1_3, Springer, Dordrecht, Dordrecht, 2000.
a
The World Bank:
Operational Innovations: Providing Immediate Funding After Natural Disasters, International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2008. a
The World Bank:
Dominican Republic,
available at:
https://data.worldbank.org/country/dominican-republic (last access: 21 June 2020), 2019. a
Ushio, T. and Kachi, M.:
Kalman Filtering Applications for Global Satellite Mapping of Precipitation (GSMaP),
in: Satellite Rainfall Applications for Surface Hydrology,
edited by: Gebremichael, M. and Hossain, F.,
Springer Netherlands, Dordrecht,
https://doi.org/10.1007/978-90-481-2915-7_7, pp. 105–123, 2010.
a
Van Nostrand, J. M. and Nevius, J. G.:
Parametric Insurance: Using Objective Measures to Address the Impacts of Natural Disasters and Climate Change,
Environ. Claim. J.,
23, 227–237,
https://doi.org/10.1080/10406026.2011.607066, 2011.
a
Visser, H., Petersen, A. C., and Ligtvoet, W.:
On the relation between weather-related disaster impacts, vulnerability and climate change,
Climatic Change,
125, 461–477,
https://doi.org/10.1007/s10584-014-1179-z, 2014.
a
Wang, L.:
Support Vector Machines: Theory and Applications, vol. 177, first edn.,
Springer, 2005. a
Wilhite, D. A.:
Drought as a natural hazard: Concepts and definitions,
in: Drought: A Global Assessment, Drought – National Drought Mitigation Center, Nebraska, Lincoln, pp. 3–18, 2000. a
Zhang, S., Zhang, C., and Yang, Q.:
Data preparation for data mining,
Appl. Artif. Intell.,
17, 375–381,
https://doi.org/10.1080/713827180, 2003.
a
