posted by user: MetaH || 1519 views || tracked by 3 users: [display]

Data Analytics 4 Environment 2021 : Data analytics for applied environmental and hydraulic modelling

FacebookTwitterLinkedInGoogle

Link: https://mssanz.org.au/modsim2021/streams.html
 
When Dec 5, 2021 - Dec 10, 2021
Where Sydney, Australia
Submission Deadline Oct 15, 2021
Categories    data analytics   computational intelligence   hydrological sciences   environmental modelling
 

Call For Papers

CALL FOR ABSTRACT

Conference:
The 24th International Congress on Modelling and Simulation (MODSIM2021)

Stream:
F. Environment and ecology

Session:
F2. Data analytics for applied environmental and hydraulic modelling

Data analytics and computational intelligence techniques are a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real-world complexities are within these areas. An integrated view of advanced data analytics and computational intelligence methodologies can be used in solving real-life hydrological and environmental problems; specifically in issues such as the surface and groundwater hydrology, hydrogeology, and hydrogeophysics as well as hydrological sciences, including water-based management, climatology, water resource systems, geomorphology, and environmental and hydraulic modelling that impact on economics and society.

The accurate estimation of these issues is critical for disaster prevention and management efforts to help reduce the potential risks of damage or loss of lives and the environment. Nowadays, to address these issues, there are widely used models based on data mining techniques such as Generalized Regression Neural Network (GRNN), Multilayer Perceptron (MLP) and Support Vector Regression (SVR) as well as General Programming models (GP). Another novel artificial intelligence methodology is weighted-average models such as Bayesian Model Averaging (BMA), and lots of other existed machine learning and fusion-based methodologies which combine predictions of individual expert systems. The weighted-average models evaluate different model predictions and assign each of them a weight based on their performance. Some of these models have the ability to reflect the uncertainty of the prediction, hence, these fusion-based techniques present advantages over other weighted-average methods, including their simplicity, rapidity and high precision.

Organizers:
- Amir H Gandomi; Professor, Data Science Institute, University of Technology Sydney, Australia. gandomi@uts.edu.au
- Mohammad Reza Nikoo; A/Professor, Department of Civil and Architectural Engineering, Sultan Qaboos University, Oman. m.reza@squ.edu.om
- Biswajeet Pradhan; Distinguished Professor, School of Information, Systems and Modelling, University of Technology Sydney, Australia. Biswajeet.Pradhan@uts.edu.au

Related Resources

DATA 2024   13th International Conference on Data Science, Technology and Applications
ISCSIC 2024   2024 8th International Symposium on Computer Science and Intelligent Control(ISCSIC 2024)
JDSA MSBE 2023   International Journal of Data Science and Analytics: Special Issue on Data Science and AI in Marine Science and Blue Economy
IEEE Xplore-EI/Scopus-CDIVP 2024   2024 4th International Conference on Digital Image and Video Processing (CDIVP 2024) -EI Compendex
IWSPA 2024   The 10th ACM International Workshop on Security and Privacy Analytics
ISC 2024   International Supercomputing Conference
CDMA 2024   8th International Conference on Data Science and Machine Learning
SIPM 2024   12th International Conference on Signal Image Processing and Multimedia
WebSci'24 PhD Symposium 2024   Web Science PhD Symposium
SI on ATD&IS III 2024   Special Issue on Advanced Technologies in Data and Information Security III, Applied Sciences, MDPI