[R-sig-Geo] Floods' Prediction Approach

[Andres Diaz]

Dear Jefferson,

The flood prone area delimitation is a science which usually involve the
hydrology + hydraulic study (in rivers) and tidal + hydrodynamics
 simulation (in coastal zones). Your approximation is pretty interesting
and I will be glad to see some results from your analysis (because it seems
is a simple approach).

Nevertheless, other approach exist to simplifying the flood maps
delimitation through topographic indexes. Attached you will find some
references in which the flood prone area delimitation is done ONLY using a
DEM (Digital Elevation Model), and in that way you can avoid to make the
complete studies for obtain the maps.

https://www.researchgate.net/publication/280308186_Samela_C_Manfreda_S_Paola_F_Giugni_M_Sole_A_and_Fiorentino_M_2015_DEM-Based_Approaches_for_the_Delineation_of_Flood-Prone_Areas_in_an_Ungauged_Basin_in_Africa_J_Hydrol_Eng_101061ASCEHE1943-55840001272_06015010

https://www.researchgate.net/publication/279243744_Flood-prone_areas_assessment_using_linear_binary_classifiers_based_on_flood_maps_obtained_from_1D_and_2D_hydraulic_models
<http://www.gits.ws/exchweb/bin/redir.asp?URL=https://www.researchgate.net/publication/279243744_Flood-prone_areas_assessment_using_linear_binary_classifiers_based_on_flood_maps_obtained_from_1D_and_2D_hydraulic_models>

If you want to comment the topic, or have problems with the algorithms
implementation, do not hesitate to contact me.

Best,


Andrés Díaz
Postdoctoral Researcher
Institute for Environmental Studies - IVM
VU University Amsterdam
De Boelelaan 1085 (visiting address)
De Boelelaan 1087 (postal address)
1081 HV Amsterdam, The Netherlands
email: andres.diazloaiza at vu.nl
Office: A567
IVM - Because the Earth matters



2016-02-04 16:14 GMT+01:00 Jefferson Ferreira-Ferreira <jecogeo at gmail.com>:

> Hello everybody. I don't know exactly if this post could be considered an
> off-topic, and sorry if so. I would like to exchange some ideas with you
> all regarding a problem and, hopefully, someone is able to help.
>
> I have a series of flood maps derived from remote sensing. Each map (13
> maps) represent the flooded area in a specific water level height (with
> binary values, 1=flooded / 0=not flooded) . I would like "predict" the
> flooded areas in intermediate water level heights where we haven't maps,
> thus trying to simulate the evolution of the flood inside the floodplain.
>
> I used a pixel-by-pixel logistic regression approach, with the dependent
> variable as the flood status (0 or 1) in each flood map and the independent
> variable as the correspondent water stage height. It predicted quite well,
> considering It's a very simple approach, giving the probabilities of a
> given pixel to be flooded at all water stage values from the drought to the
> maximum seasonal water level. But I'm getting significative errors yet
> regarding my field observations (sensors monitoring the floods scattered in
> my study area). I'm calculating the number of days per year some pixels
> remains flooded (as I know the water levels on each day in a given year)
> and I'm getting up to 120 days of difference between the predicted and
> observed days of flood in a given pixel.
>
> As I said before, It's a quite simple approach. I'm realizing that more
> sofisticated techniques could perform better. I also have some other
> variables to include, like a digital terrain model and rasters of daily
> precipitation.
>
> Has anyone some idea, some different approach (e.g. cokriging neural
> networks) ? As I'm dealing with a categoric response variable I'm not sure
> yet what is possible and what isn't. I'll continue my readings here and
> trying to find some idea. But if someone has some time to think a little
> bit and write a comment, it'll be very appreciated.
>
>  Thank you all!
> Best,
>
> --
>
> *Jefferson Ferreira-Ferreira*
>
> Geógrafo – GEOPROCESSAMENTO IDSM | Coordenadoria de TI
>
>
> Jefferson.ferreira at mamiraua.org.br
>
> *Instituto de Desenvolvimento Sustentável Mamirauá*
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