Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

Abstract

The accurate prediction of hourly runoff discharge in a river basin during typhoon events is of critical importance in operational flood control and management. This study utilizes three model approaches to predict runoff discharge in the Laonong Creek basin in southern Taiwan: the hydrological engineering center hydrological modeling system (HEC-HMS) model and two hybrid models which combine the HEC-HMS model with a genetic algorithm neural network (GANN) and an adaptive neuro-fuzzy inference system approach (ANFIS). Hourly runoff discharge data during seven heavy rainfall/typhoon events were collected for model calibration (training) and validation. Six statistical indicators [i.e., mean absolute error, root-mean-square error, coefficient of correlation, error of time to peak discharge, error of peak discharge, and coefficient of efficiency (CE)] were used to evaluate the prediction accuracy. The simulation results indicate that the HEC-HMS model cannot satisfactorily predict hourly runoff discharge during the typhoon events. Both hybrid approaches that use the HEC-HMS model in conjunction with the GANN and ANFIS models can significantly improve the prediction accuracy for the n-h-ahead runoff discharge.

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

“…Six combinations of inputs clearly demonstrate the influences of typhoon parameters on surge prediction. Reasons for further improvement over earlier works [16,60] are also given. In previous studies, inputs for local meteorological conditions would lead to higher dimensions but fail to indicate potential influences of a typhoon, hindering the learning and prediction capability of ANN-based surge models.…”

“…Among various AI branches, the most popular method over the past two decades were artificial neural networks (ANNs) which mimic the human brain to effectively learn complicated rules regarding natural phenomena from sufficient data [56]. Based upon learning, error tolerance, and generalization capability of ANNs (especially for highly nonlinear systems), extensive and successful applications in various fields (e.g., meteorology, hydrology, water resources, hydraulics, and coastal engineering) can be found in the literature ( [10,21,[57][58][59][60][61] among many others). Regarding storm surge predictions, explorations of ANN applications generally can be divided into three typical categories.…”

A Water-saving Irrigation Decision-making Model for Greenhouse Tomatoes based on Genetic Optimization T-S Fuzzy Neural Network

“…The relevant agricultural experts have researched on precision irrigation volume for greenhouse tomatoes [32,33], so this paper uses the actual irrigation volume as the standard. The performance of the algorithms is evaluated by comparing the error between the actual value and predicted value.…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

“…These systems can be established through combinations of two or more methods and techniques [36][37][38][39][40][41][42] or ensemble frameworks such as Stacking, Bagging, AdaBoost, Random Subspace, MultiBoost, Random Forests, Diverse DECORATE (Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), and Rotation Forest [43,44]. Although these ensemble-based systems often improve performances of base classifiers, the Rotation Forest outperforms the others in term of accuracy and diversity in various datasets [43,45].…”