Forecasting UHI Intensity in Sydney Using LSTM Models

Patrick Duhirwe

0

Data Analyst

ML Engineer

Python

R

TensorFlow

Data Collection and Processing: Hourly air temperature data over 18 years from 8 sites around Sydney was collected, with quality control measures applied to ensure data integrity.
Temporal UHI Variations Analysis: Investigated diurnal, monthly, seasonal, and annual variations of UHI intensity using the difference in temperature between urban areas and a suburban/rural reference location.
LSTM Model Development: Two types of LSTM models were developed; one using ambient temperature (LSTMT) and the other incorporating both air temperature and wind speed (LSTMW), to forecast UHI intensity.
Model Settings and Performance Validation: Model parameters were optimized through trial and error, and performance was validated using statistical metrics such as RMSE, MAE, and R^2.
Analysis of Results: The UHI trends and patterns were analyzed, including the impact of diurnal and seasonal changes, and spatial distribution across the different monitoring stations.
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Posted Feb 23, 2024

Developing LSTM Models for Precise UHI Effect Forecasting in Sydney, Analyzing Temporal Variations for Enhanced Urban Climate Adaptation.

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Data Analyst

ML Engineer

Python

R

TensorFlow

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