Holistic graphs and reports for all Object.h parameters in SWMM 5.1.011 for future data mining and as an aid in increasing our prior knowledge of modeling processes

This paper details describes modifications to the EPA SWMM 5.1.011 code and Delphi GUI to show more internal computational variables in the SWMM5 Graphs and Reports.  In this age of Big Data we are missing a large amount of computational knowledge due to limited graphical and report variables.  We show how it is easy to expand the output reports variables of SWMM 5.1.011 to show 100 Node, 100 Link,  50 System, 100 Hydrology and 100 LID internal computational variables.  All of these expanded variables can be graphed, made into SWMM5 tabular reports or saved to the SWMM 5 Calibration format.

These many variables increase the prior knowledge of complex network systems which helps in future machine learning; aid in understanding past versions of SWMM 5 compared to the current version; aid in comparing SWMM 5 to SWMM 5 Platforms; aid in comparing SWMM 5 to other models and simpler or older spreadsheet modeling methods.   The output variables can be used in both the Console version of SWMM 5.1.011 (which helps students using MATLAB) and the SWMM 5.1.011 Delphi GUI.  The increased variables can also be used for neural networks or genetic programming algorithms.

Due to the great structure of the SWMM5 C code these new variables can be easily added to the code in enums.h (#define MAX_LINK_RESULTS 100 for example) and in the Delphi ViewVars.TXT Code will be supplied with the paper and added to https://www.openswmm.org/