Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological v...
Saved in:
Main Authors: | Ehteram M., Ghotbi S., Kisi O., Ahmed A.N., Hayder G., Fai C.M., Krishnan M., Afan H.A., EL-Shafie A. |
---|---|
Other Authors: | 57113510800 |
Format: | Article |
Published: |
MDPI AG
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
by: Ehteram, M., et al.
Published: (2020) -
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm
by: Banadkooki F.B., et al.
Published: (2023) -
Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
by: Tao, Hai, et al.
Published: (2021) -
Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
by: Ehteram M., et al.
Published: (2023) -
River Water Suspended Sediment Predictive Analytics Using Artificial Neural Network and Convolutional Neural Network Approach: A Review
by: Khan Q., et al.
Published: (2024)