NHESS_GeosciencePaper
This code and the paper given below certifies the accuracy of discovering formulas using regression methods either linear or neliniar, using Artificial Intelligence between MMI ( modified Mercalli intensity) and related measurements:
o PGA peak ground acceleration
o and PGV peak ground velocity
o Arias intensity (Ia),
o R-> refers to the distance from the earthquake source to the site of interest, expressed in kilometers, logR=log(R)
o acceleration response spectrum (Sa), and
o cumulative absolute velocity (CAV).
Our paper (by L. Vladutu & G. Akis-Tselentis as authors) first published these formulas discovered by regression in:
"An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters
using artificial neural networks and genetic algorithms"
in Nat. Hazards Earth Syst. Sci., 10, 2527–2537, 2010
Html page is here, from which you can check it: https://nhess.copernicus.org/articles/10/2527/2010/nhess-10-2527-2010.pdf
- The first method "LinearRegression.py" retrieves exactly the coefficients from the MMI equation (9): MMI = 8.824+0.417M −7.960logR+0.380PGA +1.105Ia −0.551CAV.
This LinearRegression.py python scripts replaces all the work described in the paper by our GA-ANN combination.
The 'data.csv' is the actual data from Table I in the paper and should be placed in a sub-folder called 'data'.
- The 2nd program "nonlinearRegression.py" which uses keras with tensorflow backend verifies in an other way the accuracy of our calculus, and it discover a nonlinear dependencies between MMI and measurements (given above) with a RMSE error of approx. 0.75 %.
The requirements.txt file specifies Python (3.9/3.10) packages that one should install (using conda/pip commands).
P.S. I've also added the jupyter notebook source 'nonlinearRegression.ipynb'
and cleanPyCode_generatedFromJupyter.py which takes a Python source code generated from within a jupyter notebook with the command
!jupyter notebook --to script nonlinearRegression.ipynb
and eliminates all the "dirt" i.e. lines like # [], #[1]: .... a.s.o. and surrounding lines and generates a clean functional python source (in this case cleaned_nonlinearRegression.py).
Hope that helps !
Citation:
If you use this code or the results published in the paper, please cite our work, with details given above.
License:
The code is released under the MIT license.