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Artificial Neural Networks for Prediction of Steadman Heat Index

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Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation

Abstract

This chapter aims to design and evaluate Artificial Neural Networks (ANN), an intelligent data analytic model to predict daily Steadman Heat Index (SHI) using temperature and humidity. Using 15 stations in Australia, trend analysis for the period 1950–2017 is performed using Mann–Kendal test statistics Sen’s slope methods. Twelve ANN models are developed with a three-layer network employing different combinations of the training algorithm, hidden transfer, and output function. The Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms are utilized to determine the best combination of learning algorithms, hidden transfer, and output functions of the optimum ANN model. Assessment of model performance includes the spread and distribution of predicted SHI, Legates and McCabe Index, Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, the Willmott’s Index of Agreement, and Nash–Sutcliffe Coefficient of Efficiency. The designed model appears to be a suitable intelligent data analytic tool for weather prediction, climate change studies, and probable evaluation of dry climatic conditions in the near future replying to historical datasets to model their future values. The findings have implications for disaster risk management particularly mitigating heatwave risk and consequences on human populations, ecosystems, and other areas including agricultural, health, and wellbeing.

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Abbreviations

ACT:

Australian Capital Territory

ANN:

Artificial Neural Network

ARIMA:

Autoregressive Integrated Moving Average

AR:

Autoregressive

ARW:

Advanced Research Core of the Weather Research and Forecasting

BGFS:

Broyden–Fletcher–Gold Farb-Shanno

CFS v2:

Climate Forecast System Version 2

CPU:

Central Processing Unit

(d):

Willmott’s Index of Agreement

E:

Legates and McCabe Index

ENS:

Nash–Sutcliffe Coefficient

HI:

Heat Index

Kpa:

Kilo Pascal

LM:

Levenberg- Marquardt

MA:

Moving Average

MAE:

Mean Absolute Error

MATLAB:

Matrix Laboratory

MK:

Man-Kendall

ML:

Machine Learning

MLP:

Multi-Linear Perceptron

MLR:

Multiple Linear Regression

NSW:

New South Wales

NT:

Northern Territory

PE:

Prediction Error

PI:

Performance Indicators

QLD:

Queensland

RMSE:

Rot Mean Squared Error

SA:

South Australia

SVR:

Support Vector Regression

TAS:

Tasmania

VIC:

Victoria

WA:

Western Australia

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Acknowledgements

The conceptualization and compilation of this chapter is a result of concerted efforts of various individuals without whose help this work would not be possible. We are not able to list all the contributors here due to space constraints, but we value the significance of each contribution especially from the members of the Advanced Data Analytics: Environment Modeling and Simulation Research Group and colleagues, their seminars, discussions, ideas, and feedback. In terms of the current book chapter, we would like to express our gratitude for the contribution of all our family members and close friends for their intense support and motivation toward the completion of this work.

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Authors and Affiliations

  1. School of Sciences, University of Southern Queensland, Springfield Central, QLD, 4300, Australia

    Bhuwan Chand & Ravinesh C. Deo

  2. Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Thong Nguyen-Huy

Authors
  1. Bhuwan Chand
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  2. Thong Nguyen-Huy
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  3. Ravinesh C. Deo
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Corresponding author

Correspondence to Bhuwan Chand .

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Editors and Affiliations

  1. School of Sciences, University of Southern Queensland, Springfield Central, QLD, Australia

    Ravinesh C. Deo

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  4. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Zaher Mundher Yaseen

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Chand, B., Nguyen-Huy, T., Deo, R.C. (2021). Artificial Neural Networks for Prediction of Steadman Heat Index. In: Deo, R., Samui, P., Kisi, O., Yaseen, Z. (eds) Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5772-9_16

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