Year: 2024
Type: article
Abstract: Meandering rivers are complex geomorphic systems that play an important role in the environment. They provide habitat for a variety of plants and animals, help to filter water, and reduce flooding. However, meandering rivers are also susceptible to changes in flow, sediment transport, and erosion. These changes can be caused by natural factors such as climate change and human activities such as dam construction and agriculture. Studying meandering rivers is important for understanding their dynamics and developing effective management strategies. However, traditional methods such as numerical and analytical modeling for studying meandering rivers are time-consuming and/or expensive. Machine learning algorithms can be used to overcome these challenges and provide a more efficient and comprehensive way to study meandering rivers. In this study, we used machine learning algorithms to study the migration rate of simulated meandering rivers using semi-analytical model to investigate the feasibility of employing this new method. We then used machine learning algorithms such as multi-layer perceptron, eXtreme Gradient Boost, gradient boosting regressor, and decision tree to predict the migration rate. The results show ML algorithms can be used for prediction of migration rate, which in turn can predict the planform position.
Source: Water
DOI: 10.3390/w16172460
Institutions: Cardiff University, University of Trento, University of Tehran
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4402053910