Neural Networks for Real Time Catchment Flow Modeling and Prediction

Abstract

Accurate prediction of catchment flow has been recognized as an important measures for effective flood-risk management strategy. A neural network modeling approach was used to construct a real time catchment flow prediction model for a river basin. Two types of neural network architectures i.e. feed forward and recurrent neural networks, and three types of training algorithm i.e. Levenberg-Marquardt, Bayesian regularization, and Gradient descent with momentum and adaptive learning rate backpropagation algorithms were examined in this study. A total of six different neural network configurations were developed and examined in terms of optimum results for 1 to 5-h ahead prediction. The methods were used to predict flow in the Cilalawi River in Indonesia, and their performances were evaluated using various statistical indices. The modeling results indicate that reasonable prediction accuracy was achieved for most of models for 1-h ahead forecast with correlation >0.91. However, the model accuracy deteriorates as the lead-time increases. When compared, a 4-10-1 recurrent network and 4-4-1 feed forward network, both trained with the Levenberg-Marquardt algorithm has produced a better performances on indicators related to average goodness of prediction for the 1 to 5-h ahead river flow forecasts compared to other models. Feed forward network trained with gradient descent with momentum and adaptive learning rate backpropagation algorithm model appears to be the worst of the adaptive techniques investigated in terms of modeling performances. Thus, the results of the study suggest that recurrent and feed forward network trained with Levenberg-Marquardt are able to forecast the catchment flow up to 5 h in advance with reasonable prediction accuracy.

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

“…These models can be classified into two main types: knowledge-driven and data-driven. Each type has specific advantages and disadvantages based on data availability and modelling condition [1,2]. Knowledge-driven models are also known as physical or conceptual models.…”

Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

“…These related works have been categorized under both the hydrological approach [15][16][17] and the statistical approach which mostly applied artificial neural networks (ANNs) such as the back-propagation neural network (BPNN) [18][19][20][21][22], the state space neural network [23], the adaptive network-based fuzzy inference system (ANFIS) [24], the recurrent neural network (RNN) [21], support vector machine [1], and the radial basis function [2] as construction tools. The advantage of the short lead-time forecast is that it is fairly accurate in medium-low reservoir inflow, whereas the disadvantages are that (1) the effective forecasted lead-time is only 6 h; (2) the forecasted error in the high flow periods is high, within the range of 10% to 40% [1,2]; and (3) the time-lag circumstances of the forecasted flow rate of a longer forecasted lead-time are significant.…”

Neuro-fuzzy modeling for level prediction for the navigation sector on the Magdalena River (Colombia)

“…In this context, neural networks have become popular in rainfall and runoff modeling (e.g. Jain & Indurthy 2003; Rajurkar et al 2004), prediction of daily streamflow (Birikundavyi et al 2002;Cigizoglu 2003), prediction of river discharge (Imrie et al 2000), prediction of river stage (Thirumalaiah & Deo 1998), real-time prediction of catchment flow (Aqil et al 2007), modeling rating curve (Sudheer & Jain 2003) and for daily suspended sediment forecasting (Partal & Cigizoglu 2008), among other examples.…”

“…ANFIS has the potential to capture the benefits of both neural networks and fuzzy logic techniques in a single framework and can be employed to handle uncertainties in the systems conditions (Mehta & Jain 2008). Recently, in hydrological modeling studies, ANFIS has been demonstrating its potential in streamflow prediction (Aqil et al 2007); modelling the complex turbulent fluxes across strong shear layers (Hankin et al 2001); short-term water level prediction (Bazartseren et al 2003); groundwater vulnerability prediction (Dixon 2005); flood forecasting (Chen et al 2006); daily pan-evaporation modeling (Kisi 2006); and river flow estimation and timeseries modeling (Firat & Gü ngor 2007, 2008). …”