Suspended sediment load prediction of river systems: An artificial neural network approach

By Assefa M. Melesse, Sajjad Ahmad, Michael E. McClain, Xixi Wang, Y. Lim. 2011

307 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/e06eade0ebfd2653f95dbd6ced5cc59285f87108

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Baharin bin Ahmad
2. Khabat Khosravi
3. Himan Shahabi

In Global and Planetary Change

1. Ataollah Shirzadi
2. Baharin bin Ahmad
3. Khabat Khosravi
4. Himan Shahabi
5. N. Al‐Ansari
6. Marten Geertsema
7. John J. Clague
8. Assefa M. Melesse

In Water Science and Technology

1. Himan Shahabi
2. Assefa M. Melesse
3. Baharin bin Ahmad

Citations in the corpus

• Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, background): “Artificial neural network (ANN) is the most widely used algorithm in hydrology [28,29], but has poorer predictive power when the range of the testing dataset is not within the range of the training dataset [30–33].”