This task include:
- Develop Python code to create interpolation maps using RK and IDW with barriers for 6 water quality parameters in 5 waterbodies in seasons around selected storms.
- Develop cross-validation algorithm to compute RMSE and ME for IDW with barriers.
- Summarizing and comparing RMSE and ME between RK and IWD in the interpolated maps.
Outcomes:
- Interpolated maps by IDW using all data
- Interpolated maps by IDW using only continuous data
- Interpolated maps by RK
- RMSE and ME of all above methods
- Analyses of RMSE and ME
All deliverables are stored in the folder ..\Box\SEACAR_OEAT_FY22-23\SEACAR_WQ_Analysis_Pilot\Deliverables Task 2a
├───ArcGIS_project (ArcGIS project displaying output maps)
│ ├───Interpolated_maps
├───GIS_Data (Auxiliary GIS data)
├───Interpolated_maps (Interpolated raster maps)
│ ├───idw_All
│ ├───idw_Con
│ └───rk_All
├───result (Comparison results of RK&IDW)
│ ├───result_v1
│ ├───result_v2
│ └───result_v3
├───shapefiles (Data points used for interpolation)
│ ├───shapefiles_All
│ └───shapefiles_Con
└───StandardizedOutputs (Standardized output for Deliverables)
├───GIS_Data
├───Interpolated_maps
└───python
This task include:
- Develop Python code to create interpolation maps using RK and IDW with barriers for 6 water quality parameters in 5 waterbodies in:
- Seasons: Interpolation using RK and IDW for both continuous data and discrete data in the following three options:
- Option 1: Four seasons in the year of hurricane events
- RK interpolated maps
- IDW interpolated maps
- Python codes for the IDW & RK interpolation
- Option 2: Four seasons across two years
- RK interpolated maps
- IDW interpolated maps
- Python codes for the IDW & RK interpolation
- Option 3: Two seasons (wet/dry) in two years
- RK interpolated maps
- IDW interpolated maps
- Python codes for the IDW & RK interpolation.
- Comparison of RK vs IDW in the 3 season options
- Option 1: Four seasons in the year of hurricane events
- Monthly: Use IDW to interpolate maps for six 30-day increments prior to the storm day, and then six 30-day increments following the storm day. Only continuous data are used.
- Weekly: Use IDW to interpolate maps in 26 7-day increments prior to the storm day, and then 26 7-day increments following the storm day. Only continuous data are used.
- Python codes for generating the monthly and weekly maps
- Analysis of RMSE & ME in the monthly and weekly maps
- A preliminary gap analysis that use discrete data to validate the monthly and weekly maps interpolated using continuous data.
All deliverables of Task 2b are stored in the folder ..\Box\SEACAR_OEAT_FY22-23\SEACAR_WQ_Analysis_Pilot\Deliverables Task 2b
├───gap_analysis (Deliverables of the gap analysis)
│ └───kde_maps
│ ├───month
│ └───week
├───Interpolated_maps (Interpolated maps in seasonal, monthly and weekly intervals)
│ ├───CrossYear_IDW_All
│ ├───CrossYear_RK_All
│ ├───FourSeasons_IDW_All
│ ├───FourSeasons_RK_All
│ ├───IDW_Month
│ ├───IDW_Week
│ ├───TwoSeasons_IDW_All
│ └───TwoSeasons_RK_All
├───rmse_me (RMSE and ME in the interpolated maps)
└───shapefiles (Point data used for the interpolation)
├───CrossYear_shapefiles_All
├───FourSeasons_All
├───IDW_Month
├───IDW_Week
└───TwoSeasons_All
Calculating summary statistics () of the parameters per monitoring location and season.
-
Tables of summary statistics in the five managed areas:
-
Boxplots of summary statistics in the five managed areas:
-
Boxplots per monitoring location can also be viewed from the web map, in the "SEACAR OEAT WQ Year 2/OEAT Task 2f Box Plots" layer.
All deliverables of Task 2f are stored in the folder ..\Box\SEACAR_OEAT_FY22-23\SEACAR_WQ_Analysis_Pilot\Deliverables Task 2f
├───archive (archived boxplots)
├───boxplots (boxplots of summary statistics)
├───python (Python codes generating the statistics and boxplots)
└───statistics (tables of summary statistics)