model.ai: Artificial Intelligent modeling for estimating dust deposited...

In zf-ibrahim/myqdmi: Malaysia Quarry Dust Monitoring Artificial Intelligent

Artificial Intelligent modeling for estimating dust deposited rate

Description

model.ai A gradient boosting model (GBM) established to estimate dust deposited rate at receptor

Usage

model.ai(
  quarryInput,
  windInput,
  sourceInput,
  receptorInput,
  sourceActivity = "primaryCrusher"
)

Arguments

windInput	A data frame containing ws for windspeed (m/s) and wd for wind direction
sourceInput	A data frame containing sourceActivity is a name for the pointsource, type (the type of material processing), x for Easting, y for Northing and z for elevation height of the location.
receptorInput	A data frame containing receptor the name/label for receptor point, x for Easting, y for Northing and z for elevation height of the location.
sourceActivity	The pointsource name such as ‘primaryCrusher’ or ‘point A’ which is according to the souceInput sourceActivity.

Details

model.ai require the source and receptor location in Easting and Northing format. The sourceActivity for this model is the type of material processing activity in the quarry or if the sourceInput is the quarries location, the sourceActivity is the name of the quarries.

Value

The results will be the estimation of dust deposited rate at all the receptors from a pointsource. The unit measurement for dust deposited rate is ug/m2/month.

Author(s)

Zul Fadhli & Dr Izhar Abadi

Examples

#demo
model.ai(sourceInput, receptorInput, windInput, sourceActivity = "primaryCrusher")

zf-ibrahim/myqdmi documentation built on June 22, 2022, 6:58 a.m.