@article{zounemat2020complexities,
  title={On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Lo{\'\i}za in Puerto Rico},
  author={Zounemat-Kermani, Mohammad and Mahdavi-Meymand, Amin and Alizamir, Meysam and Adarsh, S and Yaseen, Zaher Mundher},
  journal={Journal of Hydrology},
  volume={585},
  pages={124759},
  year={2020},
  publisher={Elsevier}
}

@article{aires2023machine,
  title={Machine learning-based modeling of surface sediment concentration in Doce river basin},
  author={Aires, Uilson Ricardo Ven{\^a}ncio and da Silva, Demetrius David and Fernandes Filho, Elp{\'\i}dio In{\'a}cio and Rodrigues, Lineu Neiva and Uliana, Eduardo Morgan and Amorim, Ricardo Santos Silva and de Melo Ribeiro, Celso Bandeira and Campos, Jasmine Alves},
  journal={Journal of Hydrology},
  volume={619},
  pages={129320},
  year={2023},
  publisher={Elsevier}
}

@article{el2024physics,
  title={Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability},
  author={El Bilali, Ali and Brouziyne, Youssef and Attar, Oumaima and Lamane, Houda and Hadri, Abdessamad and Taleb, Abdeslam},
  journal={Environmental Science and Pollution Research},
  volume={31},
  number={34},
  pages={47237--47257},
  year={2024},
  publisher={Springer}
}

@article{salih2020river,
  title={River suspended sediment load prediction based on river discharge information: application of newly developed data mining models},
  author={Salih, Sinan Q and Sharafati, Ahmad and Khosravi, Khabat and Faris, Hossam and Kisi, Ozgur and Tao, Hai and Ali, Mumtaz and Yaseen, Zaher Mundher},
  journal={Hydrological Sciences Journal},
  volume={65},
  number={4},
  pages={624--637},
  year={2020},
  publisher={Taylor \& Francis}
}

@article{shakya2023predicting,
  title={Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models},
  author={Shakya, Deepti and Deshpande, Vishal and Kumar, Bimlesh and Agarwal, Mayank},
  journal={Artificial Intelligence Review},
  volume={56},
  number={9},
  pages={10067--10098},
  year={2023},
  publisher={Springer}
}

@article{nguyen2024applying,
  title={Applying a machine learning-based method for the prediction of suspended sediment concentration in the Red river basin},
  author={Nguyen, Son Q and Nguyen, Linh C and Ngo-Duc, Thanh and Ouillon, Sylvain},
  journal={Modeling Earth Systems and Environment},
  volume={10},
  number={2},
  pages={2675--2692},
  year={2024},
  publisher={Springer}
}

@article{achite2022advanced,
  title={Advanced machine learning models development for suspended sediment prediction: comparative analysis study},
  author={Achite, Mohammed and Yaseen, Zaher Mundher and Heddam, Salim and Malik, Anurag and Kisi, Ozgur},
  journal={Geocarto International},
  volume={37},
  number={21},
  pages={6116--6140},
  year={2022},
  publisher={Taylor \& Francis}
}

@article{almubaidin2023enhancing,
  title={Enhancing sediment transport predictions through machine learning-based multi-scenario regression models},
  author={Almubaidin, Mohammad Abdullah Abid and Latif, Sarmad Dashti and Balan, Kalaiarasan and Ahmed, Ali Najah and El-Shafie, Ahmed},
  journal={Results in Engineering},
  volume={20},
  pages={101585},
  year={2023},
  publisher={Elsevier}
}


@article{didkovskyi2022comparison,
  title={A comparison between machine learning and functional geostatistics approaches for data-driven analyses of sediment transport in a pre-alpine stream},
  author={Didkovskyi, Oleksandr and Ivanov, Vladislav and Radice, Alessio and Papini, Monica and Longoni, Laura and Menafoglio, Alessandra},
  journal={Mathematical Geosciences},
  volume={54},
  number={3},
  pages={467--506},
  year={2022},
  publisher={Springer}
}