@article{fdi:010084581,
  title = {{P}redicting daily suspended sediment load using machine learning and {NARX} hydro-climatic inputs in semi-arid environment},
  author = {{E}zzaouini, {M}. {A}. and {M}ah{\'e}, {G}il and {K}acimi, {I}. and {E}l {B}ilali, {A}. and {Z}erouali, {A}. and {N}afii, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. {I}ts evaluation is crucial for managing water resources. {T}he practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. {I}n this study, we attempt to apply machine-learning-based modeling ({ML}) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid {B}ouregreg basin, {M}orocco. {T}o that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. {T}he results showed that most {ML} models have good accuracy, with a {N}ash-{S}chiff efficiency ({NSE}) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the {SSL}. {F}urthermore, the models were ranked against their generalization ability ({GA}), which revealed that the developed models are good to excellent in terms of {GA}. {O}verall, the present study provides new insight into predicting the {SSL} in a semi-arid environment, such as the {B}ouregreg basin.},
  keywords = {suspended sediment load ; generalization ability ; uncertainty ; {B}ouregreg ; {M}orocco ; {MAROC} ; {ZONE} {SEMIARIDE}},
  booktitle = {},
  journal = {{W}ater},
  volume = {14},
  numero = {6},
  pages = {862 [19 p.]},
  year = {2022},
  DOI = {10.3390/w14060862},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084581},
}