Artificial neural networks
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COMP9414
Artificial Intelligence
Assignment 1 - Artificial neural networks
1 Problem context
Time Series Air Quality Prediction with Neural Networks: In this
assignment, you will delve into the realm of time series prediction using neural
network architectures. You will explore both classification and estimation
tasks using a publicly available dataset.
You will be provided with a dataset named “Air Quality,” [1] available
on the UCI Machine Learning Repository 1. We tailored this dataset for this
assignment and made some modifications. Therefore, please only use the
attached dataset for this assignment.
The given dataset contains 8,358 instances of hourly averaged responses
from an array of five metal oxide chemical sensors embedded in an air qual
ity chemical multisensor device. The device was located in the field in a
significantly polluted area at road level within an Italian city. Data were
recorded from March 2004 to February 2005 (one year), representing the
longest freely available recordings of on-field deployed air quality chemical
sensor device responses. Ground truth hourly averaged concentrations for
carbon monoxide, non-methane hydrocarbons, benzene, total nitrogen ox
ides, and nitrogen dioxide among other variables were provided by a co
located reference-certified analyser. The variables included in the dataset
1https://archive.ics.uci.edu/dataset/360/air+quality
1
are listed in Table 1. Missing values within the dataset are tagged
with -200 value.
Table 1: Variables within the dataset.
Variable Meaning
CO(GT) True hourly averaged concentration of carbon monoxide
PT08.S1(CO) Hourly averaged sensor response
NMHC(GT) True hourly averaged overall Non Metanic HydroCar
bons concentration
C6H6(GT) True hourly averaged Benzene concentration
PT08.S2(NMHC) Hourly averaged sensor response
NOx(GT) True hourly averaged NOx concentration
PT08.S3(NOx) Hourly averaged sensor response
NO2(GT) True hourly averaged NO2 concentration
PT08.S4(NO2) Hourly averaged sensor response
PT08.S5(O3) Hourly averaged sensor response
T Temperature
RH Relative Humidity
AH Absolute Humidity
2 Activities
This assignment focuses on two main objectives:
? Classification Task: You should develop a neural network that can
predict whether the concentration of Carbon Monoxide (CO) exceeds
a certain threshold – the mean of CO(GT) values – based on historical
air quality data. This task involves binary classification, where your
model learns to classify instances into two categories: above or below
the threshold. To determine the threshold, you must first calculate
the mean value for CO(GT), excluding unknown data (missing values).
Then, use this threshold to predict whether the value predicted by your
network is above or below it. You are free to choose and design your
own network, and there are no limitations on its structure. However,
your network should be capable of handling missing values.