Prediction of Suspended Sediment Concentration Using ANFIS with the Bacterial Foraging Optimization Algorithm

DOI

10.2991/978-94-6463-336-8_23How to use a DOI?

Keywords

Bacterial foraging optimization algorithm; ANFIS; ANN; suspended sediment; modeling

Abstract

This study combined ANFIS with a bacterial foraging optimization algorithm (ANFIS-BFO) to predict the daily suspended sediment concentration based on the daily series data observed at the Rio Valenciano hydrological station near Puerto Rico, USA. Meanwhile, ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM), artificial neural network (ANN), and the sediment rating curve (SRC) are utilized for the prediction of the same flow discharge-suspended sediment concentration (SSC) daily series data. The root mean square error (RMSE), mean root square error (MRSE), and coefficient of determination (R2) were adopted as the evaluation indicators of the prediction performance of each model. According to the different settings of the input and output variables, the predictions for four different scenarios were carried out. The comparative analysis results show that we can gain the best prediction results when the current day's flow discharge is used as the input and the current day's SSC is used as the output for the hydrological station in the study area. For the Rio Valenciano Station, the MRSE value of the ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC is, respectively, 2.2172, 2.5389, 2.6627, 2.7549, 2.7994 and 3.7882. It can be inferred that ANFIS-BFO embodies better prediction results than all other models. ANFIS-SC and ANFIS-FCM have slightly superior prediction performance to ANFIS-GP. ANFIS-GP, ANFIS-SC, and ANFIS-FCM have slightly superior prediction performance to ANN.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinsheng Fan
AU  - Qiushi Luo
AU  - Yuchuan Bai
PY  - 2023
DA  - 2023/12/30
TI  - Prediction of Suspended Sediment Concentration Using ANFIS with the Bacterial Foraging Optimization Algorithm
BT  - Proceedings of the 2023 9th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2023)
PB  - Atlantis Press
SP  - 209
EP  - 216
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-336-8_23
DO  - 10.2991/978-94-6463-336-8_23
ID  - Fan2023
ER  -