Amin Talei

A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling

Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Although many studies have been carried out using this approach in pattern recognition and signal processing, few studies have been undertaken to evaluate their performances in hydrologic modeling, specifically rainfall-runoff (R-R) modeling. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in event-based R-R modeling in order to evaluate the capabilities of this method for a sub-catchment of Kranji basin in Singapore. Approximately two years of rainfall and runoff data which from 66 separate rainfall events were analyzed in this study. Two different approaches in the selection criteria for calibration events were adopted and the performance of an ANFIS R-R model was compared against an established physically-based model called Storm Water Management Model (SWMM) in R-R modeling. The results of this study show that the selected neuro-fuzzy-computational technique (ANFIS) is comparable to SWMM in event-based R-R modeling. In addition, ANFIS is found to be better at peak flow estimation compared to SWMM. This study demonstrates the promising potential of neuro-fuzzy-computationally inspired hybrid tools in R-R modeling and analysis.

Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) for River Stage Prediction in a Tropical Catchment

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

Water level forecasting using neuro-fuzzy models with local learning

The global learning method is widely used to train data-driven models for hydrological forecasting. The drawback of global models is that a long data record is required and the model is not easily adapted once it is trained. This study investigated the local learning approach applied in the dynamic evolving neural-fuzzy inference system (DENFIS) to provide 5-lead-day water level forecasts for the Mekong River. The local learning method focuses on the relationship between input and output variables at the most recent state. The results obtained from DENFIS were found to be better than results obtained from adaptive neuro-fuzzy inference system, which uses global learning approach, and the unified river basin simulator model. Local learning provides continuous model updating, and the results obtained in this study show that local learning is a promising tool for water level forecasting in real-time flood warning applications.