MODELING OF ANISOTROPIC SEMIVARIOGRAM USING ITERATIVE REWEIGHTED LEAST SQUARES AS A PARAMETER ESTIMATION METHOD (CASE STUDY: HEAVY METAL CONTENT OF FE, MN, AND PB IN WELL WATER IN BANDUNG REGENCY)
Well water is one of the clean water sources used by residents of Bandung Regency in their daily lives. The heavy metals content in well water that is consumed can affect their health if it exceeds the maximum levels stated in the Regulation of the Health Ministry of Indonesia 2023 number 2. Wate...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84070 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Well water is one of the clean water sources used by residents of Bandung Regency
in their daily lives. The heavy metals content in well water that is consumed can
affect their health if it exceeds the maximum levels stated in the Regulation of the
Health Ministry of Indonesia 2023 number 2. Water flows from one place to another
indicating that the heavy metals content in it could have a spatial relation which
can be influenced by distance and direction. The analysis that can be used to see
the relation between locations is an anisotropic semivariogram. The data studied
is heavy metal content data at 181 locations spread across 7 districts. This study
aims to build the best anisotropic semivariogram model using Iterative Reweighted
Least Squares as the parameter estimation method which is a weighted numerical
solution for data with outliers. The experimental semivariogram was calculated
using the Cressie-Hawkins approach and the distance lag classified by the Sturges
and Scott rules. There was a significant decrease in the value of the Sturges rule
which can reduce the modeling accuracy, so only the result of the Scott rule is
modeled with the theoretical semivariogram of the Exponential and Gauss models.
The best anisotropic model for the Fe and Pb metal variables was the geometric
Gauss, whereas for the Mn metal variable was the zonal Gauss with each Sum of
Squared Errors being smaller than the Exponential model. The best model was
applied to interpolate values at three unobserved district points using Ordinary
Kriging because the average was unknown. The interpolated values of the three
variables were within the range of the actual data and did not differ significantly
from the surrounding observed values.
|
|---|