Artificial neural networks for prediction of Steadman Heat Index
Edited book (chapter)
Chand, Bhuwan, Nguyen-Huy, Thong and Deo, Ravinesh C.. 2021. "Artificial neural networks for prediction of Steadman Heat Index." Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher (ed.) Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Singapore. Springer. pp. 293-357
| Chapter Title | Artificial neural networks for prediction of Steadman Heat Index |
|---|---|
| Book Chapter Category | Edited book (chapter) |
| ERA Publisher ID | 3337 |
| Book Title | Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation |
| Authors | Chand, Bhuwan (Author), Nguyen-Huy, Thong (Author) and Deo, Ravinesh C. (Author) |
| Editors | Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher |
| Page Range | 293-357 |
| Series | Springer Transactions in Civil and Environmental Engineering |
| Chapter Number | 16 |
| Number of Pages | 65 |
| Year | 2021 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISBN | 9789811557712 |
| 9789811557729 | |
| ISSN | 2363-7633 |
| 2363-7641 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-15-5772-9_16 |
| Web Address (URL) | https://link.springer.com/chapter/10.1007/978-981-15-5772-9_16 |
| 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. |
| Keywords | data-driven; artificial neural networks; heatwaves prediction; backpropagation algorithms; hidden transfer |
| ANZSRC Field of Research 2020 | 410402. Environmental assessment and monitoring |
| 460207. Modelling and simulation | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Sciences |
| Centre for Applied Climate Sciences | |
| Institution of Origin | University of Southern Queensland |
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Nguyen-Huy, Thong, Deo, Ravinesh C., Khan, Shahjahan, Devi, Aruna, Adeyinka, Adewuyi Ayodele, Apan, Armando A. and Yaseen, Zaher Mundher. 2022. "Student Performance Predictions for Advanced Engineering Mathematics Course With New Multivariate Copula Models." IEEE Access. 10, pp. 45112 -45136. https://doi.org/10.1109/ACCESS.2022.3168322Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia
Murray, Xavier, Apan, Armando, Deo, Ravinesh and Maraseni, Tek. 2022. "Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia." Geocarto International. 37 (26), pp. 11223-11252. https://doi.org/10.1080/10106049.2022.2048902Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization
Ahmadianfar, Iman, Noori, Ramzia Majeed, Togun, Hussein, Falah, Mayadah W., Homod, Raad Z., Fu, Minglei, Halder, Bijay, Deo, Ravinesh and Yaseen, Zaher Mundher. 2022. "Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization." Knowledge-Based Systems. 250, pp. 1-18. https://doi.org/10.1016/j.knosys.2022.109048Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach
Khosravi, Khabat, Golkarian, Ali, Melesse, Assefa M. and Deo, Ravinesh C.. 2022. "Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach." Journal of Hydrology. 610, pp. 1-14. https://doi.org/10.1016/j.jhydrol.2022.127963Delineating the Crop-Land Dynamic due to Extreme Environment Using Landsat Datasets: A Case Study
Halder, Bijay, Bandyopadhyay, Jatisankar, Afan, Haitham Abdulmohsin, Naser, Maryam H., Abed, Salwan Ali, Khedher, Khaled Mohamed, Falih, Khaldoon T., Deo, Ravinesh, Scholz, Miklas and Yaseen, Zaher Mundher. 2022. "Delineating the Crop-Land Dynamic due to Extreme Environment Using Landsat Datasets: A Case Study." Agronomy. 12 (6), pp. 1-23. https://doi.org/10.3390/agronomy12061268Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction
Ali, Mumtaz, Deo, Ravinesh C., Xiang, Yong, Prasad, Ramendra, Li, Jianxin, Farooque, Aitazaz and Yaseen, Zaher Mundher. 2022. "Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction." Scientific Reports. 12 (1), pp. 1-23. https://doi.org/10.1038/s41598-022-09482-5Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network
Deo, Ravinesh C., Grant, Richard H., Webb, Ann, Ghimire, Sujan, Igoe, Damien P., Downs, Nathan J., Al-Musaylh, Mohanad S., Parisi, Alfio V. and Soar, Jeffrey. 2022. "Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network." Stochastic Environmental Research and Risk Assessment. 36, p. 3183–3220. https://doi.org/10.1007/s00477-022-02188-0Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction
Ghimire, Sujan, Deo, Ravinesh C, Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction." Renewable Energy. 190, pp. 408-424. https://doi.org/10.1016/j.renene.2022.03.120Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model
Ghimire, Sujan, Nguyen-Huy, Thong, Deo, Ravinesh C., Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model." Sustainable Materials and Technologies. 32, pp. 1-24. https://doi.org/10.1016/j.susmat.2022.e00429Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia
Ghimire, Sujan, Bhandari, Binayak, Casillas-Perez, David, Deo, Ravinesh C. and Salcedo-sanz, Sancho. 2022. "Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia." Engineering Applications of Artificial Intelligence. 112, pp. 1-26. https://doi.org/10.1016/j.engappai.2022.104860Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms
Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms." Applied Energy. 316, pp. 1-25. https://doi.org/10.1016/j.apenergy.2022.119063Machine learning regression and classification methods for fog events prediction
Castillo-Boton, C., Casillas-Perez, D., Casanova-Mateo, C., Ghimire, S., Cerro-Prada, E., Gutierrez, P. A., Deo, R. C. and Salcedo-sanz, S.. 2022. "Machine learning regression and classification methods for fog events prediction." Atmospheric Research. 272, pp. 1-23. https://doi.org/10.1016/j.atmosres.2022.106157Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing
Riaz, Farina, Abdulla, Shahab, Ni, Wei, Radfar, Mohsen, Deo, Ravinesh and Hopkins, Susan. 2022. "Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing." Quantum Australia Conference 2022. Online 23 - 25 Feb 2022 Toowoomba, Australia. https://doi.org/10.13140/RG.2.2.34754.66245A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar
Nguyen-Huy, Thong, Kath, Jarrod, Nagler, Thomas, Khaung, Ye, Aung, Thee Su Su, Mushtaq, Shahbaz, Marcussen, Torben and Stone, Roger. 2022. "A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar." Remote Sensing Applications: Society and Environment. 26, pp. 1-19. https://doi.org/10.1016/j.rsase.2022.100733Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David, Salcedo-sanz, Sancho, Sharma, Ekta and Ali, Mumtaz. 2022. "Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction." Measurement. 202, pp. 1-22. https://doi.org/10.1016/j.measurement.2022.111759Basin management inspiration from impacts of alternating dry and wet conditions on water production and carbon uptake in Murray-Darling Basin
Lu, Zhixiang, Feng, Qi, Wei, Yongping, Zhao, Yan, Deo, Ravinesh C., Xie, Jiali, Zhou, Sha, Zhu, Meng and Xu, Min. 2022. "Basin management inspiration from impacts of alternating dry and wet conditions on water production and carbon uptake in Murray-Darling Basin." Science of the Total Environment. 851 (Part 2), pp. 1-8. https://doi.org/10.1016/j.scitotenv.2022.158359Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
Ahmed, Abul Abrar Masrur, Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J., Devi, Aruna, Barua, Prabal D. and Yaseen, Zaher M.. 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model." Sustainability. 14 (17), pp. 1-27. https://doi.org/10.3390/su141711070Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors
Ahmed, A. A. Masrur, Sharma, Ekta, Jui, S. Janifer Jabin, Deo, Ravinesh C., Nguyen-Huy, Thong and Ali, Mumtaz. 2022. "Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors." Remote Sensing. 14 (5), pp. 1-24. https://doi.org/10.3390/rs14051136Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System
Prasad, Salvin S., Deo, Ravinesh C., Downs, Nathan, Igoe, Damien, Parisi, Alfio V. and Soar, Jeffrey. 2022. "Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System." IEEE Access. 10, pp. 24704-24720. https://doi.org/10.1109/ACCESS.2022.3153475Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data
Jayasinghe, W. J. M. Lakmini Prarthana, Deo, Ravinesh C., Ghahramani, Afshin, Ghimire, Sujan and Raj, Nawin. 2022. "Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data." Journal of Hydrology. 607, pp. 1-19. https://doi.org/10.1016/j.jhydrol.2022.127534Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects
Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Kumar, Adarsh, Malik, Tabarak, Jawad, Ali H., Khedher, Khaled Mohamed, Deo, Ravinesh C. and Yaseen, Zaher Mundher. 2022. "Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects." Journal of Environmental Management. 309, pp. 1-16. https://doi.org/10.1016/j.jenvman.2022.114711An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis
Al-Hadeethi, Hanan, Abdulla, Shahab, Diykh, Mohammed, Deo, Ravinesh C. and Green, Jonathan H.. 2022. "An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis." Frontiers in Neuroinformatics. 15, pp. 1-15. https://doi.org/10.3389/fninf.2021.808339Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
Ghimire, Sujan, Deo, Ravinesh C., Wang, Hua, Al-Musaylh, Mohanad S., Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results." Energies. 15 (3), pp. 1-39. https://doi.org/10.3390/en15031061Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals
Diykh, Mohammed, Miften, Firas Sabar, Abdulla, Shahab, Deo, Ravinesh C., Siuly, Siuly, Green, Jonathan H. and Oudah, Atheer Y.. 2022. "Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals." Measurement. 190 (110731), pp. 1-13. https://doi.org/10.1016/j.measurement.2022.110731General equilibrium impact evaluation of food top-up induced by households’ renewable power self-supply in 141 regions
Nguyen, Duong Binh, Nong, Duy, Simshauser, Paul and Nguyen-Huy, Thong. 2022. "General equilibrium impact evaluation of food top-up induced by households’ renewable power self-supply in 141 regions." Applied Energy. 306 (Part B), pp. 1-13. https://doi.org/10.1016/j.apenergy.2021.118126Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data
O'Sullivan, Cherie M., Ghahramani, Afshin, Deo, Ravinesh C., Pembleton, Keith, Khan, Urooj and Tuteja, Narendra. 2022. "Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data." Science of the Total Environment. 809, pp. 1-15. https://doi.org/10.1016/j.scitotenv.2021.151139Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in Australia
Nguyen-Huy, Thong. 2020. "Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in Australia." Bulletin of the Australian Mathematical Society. 101 (1), pp. 166-169. https://doi.org/10.1017/S0004972719001217Drought Outlook Products Review
Cobon, David, Nguyen-Huy, Thong and Reardon-Smith, Kate. 2019. Drought Outlook Products Review. Toowoomba, Australia. University of Southern Queensland.Domino effect of climate change over two millennia in ancient China’s Hexi Corridor
Feng, Qi, Yan, Linshan, Deo, Ravinesh C., AghaKouchak, Amir, Adamowski, Jan F., Stone, Roger, Yin, Zhenliang, Liu, Wei, Si, Jianhua, Wen, Xiaohu, Zhu, Meng and Cao, Shixiong. 2019. "Domino effect of climate change over two millennia in ancient China’s Hexi Corridor." Nature Sustainability. 2, pp. 957-961. https://doi.org/10.1038/s41893-019-0397-9Bat algorithm for dam–reservoir operation
Ethteram, Mohammad, Mousavi, Sayed-Farhad, Karami, Hojat, Farzin, Saeed, Deo, Ravinesh, Othman, Faridah Binti, Chau, Kwok-Wing, Sarkamaryan, Saeed, Singh, Vijay P. and El-Shafie, Ahmed. 2018. "Bat algorithm for dam–reservoir operation." Environmental Earth Sciences. 77 (13), pp. 1-15. https://doi.org/10.1007/s12665-018-7662-5Characteristics of ecosystem water use efficiency in a desert riparian forest
Ma, Xiaohong, Feng, Qi, Su, Yonghong, Yu, Tengfei and Deo, Ravinesh C.. 2018. "Characteristics of ecosystem water use efficiency in a desert riparian forest." Environmental Earth Sciences. 77 (358). https://doi.org/10.1007/s12665-018-7518-zThe influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River
Diop, Lamine, Bodian, Ansoumana, Djaman, Koffi, Yaseen, Zaher Mundher, Deo, Ravinesh C., El-Shafie, Ahmed and Brown, Larry C.. 2018. "The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River." Environmental Earth Sciences. 77 (5). https://doi.org/10.1007/s1266Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River
Raheli, Bahare, Aalami, Mohammad Taghi, El-Shafie, Ahmed, Ghorbani, Mohammad Ali and Deo, Ravinesh C.. 2017. "Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River." Environmental Earth Sciences. 76 (14). https://doi.org/10.1007/s12665-017-6842-zMethodology for producing the Drought Monitor
Pudmenzky, Christa, Guillory, Laura, Cobon, David H. and Nguyen-Huy, Thong. 2020. Methodology for producing the Drought Monitor. Toowoomba, Queensland. University of Southern Queensland.Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program
Cobon, David, Jarvis, Chelsea, Reardon-Smith, Kate, Guillory, Laura, Pudmenzky, Christa, Nguyen-Huy, Thong, Mushtaq, Shahbaz and Stone, Roger. 2021. "Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program." The Rangeland Journal. 43 (3), pp. 87-100. https://doi.org/10.1071/RJ20074Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots
Sharma, Ekta, Deo, Ravinesh C., Soar, Jeffrey, Prasad, Ramendra, Parisi, Alfio V. and Raj, Nawin. 2022. "Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots." Atmospheric Environment. 279, pp. 1-13. https://doi.org/10.1016/j.atmosenv.2022.119111New double decomposition deep learning methods for river water level forecasting
Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Feng, Qi, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "New double decomposition deep learning methods for river water level forecasting." Science of the Total Environment. 831, pp. 1-21. https://doi.org/10.1016/j.scitotenv.2022.154722Global disparities in agricultural climate index-based insurance research
Adeyinka, Adewuyi Ayodele, Kath, Jarrod, Nguyen-Huy, Thong, Mushtaq, Shahbaz, Souvignet, Maxime, Range, Matthias and Barratt, Jonathan. 2022. "Global disparities in agricultural climate index-based insurance research." Climate Risk Management. 35, pp. 1-15. https://doi.org/10.1016/j.crm.2022.100394Drought outlook validation using ACCESS-S2 hindcast
Nguyen-Huy, Thong and Cobon, David. 2022. Drought outlook validation using ACCESS-S2 hindcast. Northern Australia Climate Program (NACP).Systemic risk management in farming - geographic distribution and diversity
Nguyen-Huy, Thong. 2022. "Systemic risk management in farming - geographic distribution and diversity." Future Drought Fund’s 2022 Science to Practice Forum. 07 - 08 Jun 2022 Australia.