Research on water level prediction for great lakes based on GAMLSS-GLO model

Dong Niu; Yi Lu

doi:10.1117/12.3035206

21 July 2024 Research on water level prediction for great lakes based on GAMLSS-GLO model

Dong Niu, Yi Lu

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 1321942 (2024) https://doi.org/10.1117/12.3035206
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

The Great Lakes are the largest group of freshwater lakes in the world and are vital to many of the two countries' largest cities, supporting diverse uses and ecosystems. Although there are regulatory mechanisms of human control, natural phenomena such as rainfall and evaporation are still difficult to predict and control. Regional policies and environmental changes can also affect water levels, which in turn affect ecosystems and human communities. In this work, through the temporal analysis of short-term water levels, the researchers revealed the trend and pattern of water level change. Then, by comparing ARIMA model and PEARIMA model, the performance of different models in predicting water level is evaluated, and the accuracy of prediction is further improved and analyzed the data from 2000 to 2010. Further, we find the water level sequence autocorrelation coefficient and find the dependency in the confidence level of 95% is significant. Subsequently, we established the flow detection model of GAMLSS-GLO and random forest algorithm to detect the dynamic water flow of five lakes. From our extensive simulations, we can observe that our model can precisely predict the water flow changing in Great Lakes and provide reasonable management strategies for controlling the flows.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dong Niu and Yi Lu "Research on water level prediction for great lakes based on GAMLSS-GLO model", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 1321942 (21 July 2024); https://doi.org/10.1117/12.3035206

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KEYWORDS

Data modeling

Principal component analysis

Data conversion

Climate change

Water

Performance modeling

Rain

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