Counterpropagation fuzzy–neural network for streamflow reconstruction

This study combines neural networks and fuzzy arithmetic to present a counterpropagation fuzzy–neural network (CFNN) for streamflow reconstruction. The CFNN has a rule‐based control, a modified self‐organizing counterpropagation network, and a fuzzy control predictor. It can generate rules automatically by increasing the training data to improve the accuracy of streamflow reconstruction. The CFNN establishes the input and output relationship of a watershed without set‐up parameters. The parameters are estimated systematically by the approach converging to an optimal solution. One sequence of data generated by the Monte Carlo method is used to demonstrate the accuracy of the CFNN. The streamflow data of the Da‐chia River, in central Taiwan, is also used to evaluate the performances of the CFNN. The results indicate the reliability and accuracy of the CFNN for streamflow reconstruction. Copyright © 2001 John Wiley & Sons, Ltd.     

Developing a hybrid multi‐model for peak flood forecasting

The present study aims to develop a hybrid multi‐model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low‐level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi‐layer feed‐forward network, the main part is a fuzzy system based on the first‐order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back‐propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi‐model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro‐genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi‐model can be used successfully with an efficiency index (EI) more than 0·95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0·90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi‐model helps to improve the accuracy of flood peak. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparative study of neural network,fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains

This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)‐based approaches for daily rainfall‐runoff modelling. This study also investigates the potential of Takagi‐Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall‐runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall‐runoff modelling. Using the rainfall‐runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN‐based models. Copyright © 2010 John Wiley & Sons, Ltd.     

A systematic approach to the input determination for neural network rainfall–runoff models

Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs are removed. Then an adequate ANN model, which only includes highly relevant inputs, is constructed. Unlike the trial‐and‐error procedure, the proposed approach is more systematic and avoids unnecessary trials. To demonstrate the effectiveness of the proposed approach, an application to actual typhoon events is presented. The results show that the proposed ANN model, which is constructed by the proposed approach, has advantages over those obtained by the trial‐and‐error procedure. The proposed ANN model has a simpler architecture, needs less training time, and performs better. The proposed ANN model is recommended as an alternative to existing rainfall–runoff ANN models. Copyright © 2007 John Wiley & Sons, Ltd.     

A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach

In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.     

Evaluation of nonlinear models for precipitation forecasting

Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.     

A nonlinear data‐driven model for synthetic generation of annual streamflows

A hybrid model that blends two non‐linear data‐driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non‐parametric resampling technique MBB to obtain innovations, which are then added back to the ANN‐modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN‐based non‐linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN‐based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric‐based hybrid model (LPHM), owing to the effective capturing of the non‐linearities. Moreover, the ANNHM, being a completely data‐driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non‐linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparison of four updating models for real-time river flow forecasting

Abstract

Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) updating model. A fuzzy autoregressive-threshold (FU-AR-TS) updating model is proposed as the third form of model, the fourth and final error-forecast updating model applied being the artificial neural network (ANN) model. In the application of these four updating models, the lumped soil moisture accounting and routing (SMAR) conceptual model has been selected to simulate the observed discharge series on 11 selected test catchments. As expected, it is found that all of these four updating models are very successful in improving the flow forecast accuracy, when operating in real-time forecasting mode. A less expected, but nonetheless welcome, result is that the three updating models having the most parameters, i.e. AR-TS, FU-AR-TS, and ANN, do not show any considerable advantages in improving the real-time flow forecast efficiency over that of the simple standard AR model. Thus it is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the AR model.     

基于减法聚类模糊神经网络的砂土液化势判别

砂土地震液化问题是岩土地震工程学的重要研究课题之一。在分析模糊神经网络原理的基础上,利用减法聚类算法对自适应模糊推理系统进行优化,并建立了砂土地震液化的模糊神经网络模型。然后,将该模型用于实际工程的砂土液化判别中,并与传统砂土液化判别方法结果进行对比。判别结果表明:文中建立的模糊神经网络模型具有较强的学习功能,用于砂土地震液化判别中是可行的和有效的。     

The strategy of building a flood forecast model by neuro‐fuzzy network

A methodology is proposed for constructing a flood forecast model using the adaptive neuro‐fuzzy inference system (ANFIS). This is based on a self‐organizing rule‐base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall‐runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self‐constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back‐propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd.     

Uncertainties in real‐time flood forecasting with neural networks

Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r² and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.     

Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN)

A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS‐X computer program. Then, the mechanistic model was combined with a feed‐forward back‐propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.     

Predicting catchment flow in a semi‐arid region via an artificial neural network technique

A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of this method for non‐linear modelling has been demonstrated in several scientific fields such as biology, geology, chemistry and physics. The performance of the developed neural network‐based model was compared against multiple linear regression‐based model using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modelling appears to be a promising technique for the prediction of flow for catchments in semi‐arid regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate. Copyright © 2004 John Wiley & Sons, Ltd.     

Modelling evapotranspiration using discrete wavelet transform and neural networks

This study combines wavelet transforms and feed‐forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub‐time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre‐processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi‐linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied successfully for evapotranspiration modelling from climatic data. Copyright © 2009 John Wiley & Sons, Ltd.     

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir.     

Applications of rough set theory to river environment quality evaluation in China

Very often, inexact, uncertain, or vague hydrological data are encountered in water researches. Statistical and fuzzy set theories are applied to deal with hydrological uncertainty problems. In this paper, the basic concept of the rough set theory is introduced and its application to hydrological data is illustrated. The proposed method is applied to water environment and environment quality evaluation of the Hang Jiang River, a major branch of the Yangtze River, in China. Our numerical applications suggest that the rough set theory is a useful tool for analysis of inexact, uncertain, or vague hydrological data.     

Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach

Accurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high‐frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet‐neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub‐series with low (approximation) and high (details) frequency, and these sub‐series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimating anisotropic aquifer parameters by artificial neural networks

In recent years, many approaches have been developed using the artificial neural networks (ANN) model incorporated with the Theis analytical solution to estimate the effective hydrological parameters for homogeneous and isotropic porous media, such as the Lin and Chen approach (ANN approach) and the principal component analysis (PCA)‐ANN approach. The above methods assume a full superimposition of the type curve and the observed drawdown and try to use the first time‐drawdown data as a match point to make a fine approximation of the effective parameters. However, using first time‐drawdown data or early time‐drawdown data does not always allow for an accurate estimation of the hydrological parameters, especially for heterogeneous and anisotropic aquifers. Therefore, this article corrects the concept of the superimposed plot by modifying the ANN approach and the PCA‐ANN approach, as well as incorporating the Papadopoulos analytical solution, to estimate the transmissivities and storage coefficient for anisotropic, homogeneous aquifers. The ANN model is trained with 4000 training sets of the well function, and tested with 1000 sets and 300 sets of synthetic time‐drawdown generated from the homogeneous and heterogeneous parameters, respectively. In situ observation data from the time‐drawdown at station Shi‐Chou on the Choushui River alluvial fan, Taiwan, is further adopted to test the applicability and reliability of the proposed methods, as well as provide a basis for comparison with the Straight‐line method and the Type‐curve method. Results suggest that both of the modified methods perform better than the original ones, and using late time‐drawdown to optimize the effective parameters is shown to be better than using early time‐drawdown. Additionally, results indicate that the modified ANN approach is better than the modified PCA‐ANN approach in terms of precision, while the efficiency of the modified PCA‐ANN approach is approximately three times better than that of the modified ANN approach. Copyright © 2010 John Wiley & Sons, Ltd.