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.     

Predicting and forecasting flow discharge at sites receiving significant lateral inflow

Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling hourly and daily open‐water evaporation rates in areas with an equatorial climate

Evaporation rate estimation is important for water resource studies. Previous studies have shown that the radiation‐based models, mass transfer models, temperature‐based models and artificial neural network (ANN) models generally perform well for areas with a temperate climate. This study evaluates the applicability of these models in estimating hourly and daily evaporation rates for an area with an equatorial climate. Unlike in temperate regions, solar radiation was found to correlate best with pan evaporation on both the hourly and daily time‐scales. Relative humidity becomes a significant factor on a daily time‐scale. Among the simplified models, only the radiation‐based models were found to be applicable for modelling the hourly and daily evaporations. ANN models are generally more accurate than the simplified models if an appropriate network architecture is selected and a sufficient number of data points are used for training the network. ANN modelling becomes more relevant when both the energy‐ and aerodynamics‐driven mechanisms dominate, as the radiation and the mass transfer models are incapable of producing reliable evaporation estimates under this circumstance. Copyright © 2006 John Wiley & Sons, Ltd.     

A multivariate non‐parametric model for synthetic generation of daily streamflow

A p‐order multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of data‐driven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or Pearson III) and the form of dependence (linear or non‐linear). The p‐order multivariate kernel density model is a non‐parametric method for synthesis of streamflow. The model is more flexible than conventional parametric models used in stochastic hydrology. The effectiveness and satisfactoriness of this model are illustrated through its application to the simultaneous synthetic generation of daily streamflow from Pingshan station and Yibin‐Pingshan region (Yi‐Ping region) of the Jinsha River in China. Copyright © 2007 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.     

Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models

Abstract

New wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. 

Editor D. Koutsoyiannis; Associate editor L. See

Citation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324.     

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.     

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.     

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.     

Rainfall‐runoff analysis based on competing linear impulse responses: decomposition of rainfall‐runoff processes

Many recent studies have successfully used neural networks for non‐linear rainfall‐runoff modelling. Due to fundamental limitation of linear structures, approaches employing linear models have been generally considered inferior to the neural network approaches in this area. However, the authors believe that with an appropriate extension, the concept of linear impulse responses can be a viable tool since it enables one to understand underlying dynamics of rainfall‐runoff processes. In this paper, the use of competing impulse responses for rainfall‐runoff analysis is proposed. The proposed method is based on the switch over of competing linear impulse‐responses, each of which satisfies the constraints of non‐negativity and uni‐modality. The computational analyses performed for the rainfall‐runoff data in the Seolma‐Chun experimental basin, Korea showed that the proposed method can yield promising results. Considering the basin characteristics as well as the results from this study, it may be concluded that three impulse responses are enough for rainfall‐runoff analysis. Copyright © 2007 John Wiley & Sons, Ltd.     

Non‐parametric–parametric model for random uncertainties in non‐linear structural dynamics: application to earthquake engineering

This paper deals with the transient response of a non‐linear dynamical system with random uncertainties. The non‐parametric probabilistic model of random uncertainties recently published and extended to non‐linear dynamical system analysis is used in order to model random uncertainties related to the linear part of the finite element model. The non‐linearities are due to restoring forces whose parameters are uncertain and are modeled by the parametric approach. Jayne's maximum entropy principle with the constraints defined by the available information allows the probabilistic model of such random variables to be constructed. Therefore, a non‐parametric–parametric formulation is developed in order to model all the sources of uncertainties in such a non‐linear dynamical system. Finally, a numerical application for earthquake engineering analysis is proposed concerning a reactor cooling system under seismic loads. Copyright © 2003 John Wiley & Sons, Ltd.     

Short‐term inflow forecasting using an artificial neural network model

The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.     

Predicting Diel,Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll‐a Using Regression Models and Neural Networks

Human‐induced and natural interruptions with continuous streams of observational data necessitate the development of gap‐filling and prediction strategies towards better understanding, monitoring and management of aquatic systems. This study quantified the efficacy of multiple non‐linear regression (MNLR) versus artificial neural network (ANN) models as well as the temporal partitioning of diurnal versus nocturnal data for the predictions of chlorophyll‐a (chl‐a) and dissolved oxygen (DO) dynamics. The temporal partitioning increased the predictive performances of the best MNLR models of diurnal DO by 45% and nocturnal DO by 4%, relative to the best diel MNLR model of diel DO ($r_{{\rm adj}}^{2} = 68.8\%$ ). The ANN‐based predictions had a higher predictive power than the MNLR‐based predictions for both chl‐a and DO except for diurnal DO dynamics. The best ANNs based on independent validations were multilayer perceptron (MLP) for diel chl‐a, generalized feedforward (GFF) for diurnal and nocturnal chl‐a, MLP for diel DO, GFF for diurnal DO, and MLP for nocturnal DO.     

Modelling storage‐driven connectivity between landscapes and riverscapes: towards a simple framework for long‐term ecohydrological assessment

The importance of conceptualizing the dynamics of storage‐driven saturation area connectivity in runoff generation has been central to the development of TOPMODEL and similar low parameterized rainfall–runoff models. In this contribution, we show how we developed a 40‐year hydrometric data base to simulate storage–discharge relationships in the Girnock catchment in the Scottish Highlands using a simple conceptual model. The catchment is a unique fisheries reference site where Atlantic salmon populations have been monitored since 1966. The modelling allowed us to track storage dynamics in hillslopes, the riparian zone and groundwater, and explicitly link non‐linear changes of streamflows to landscape storage and connectivity dynamics. This provides a fundamental basis for understanding how the landscape and riverscape are hydrologically connected and how this regulates in‐stream hydraulic conditions that directly influence salmonids. We use the model to simulate storage and discharge dynamics over the 40‐year period of fisheries records. The modelled storage‐driven connectivity provides an ecohydological context for understanding the dynamics in stream flow generation which determine habitat hydraulics for different life stages of salmon population. This new, long‐term modelling now sets this variability in the riverscape in a more fundamental context of the inter‐relationships between storage in the landscape and stream flow generation. This provides a simple, robust framework for future ecohydrological modelling at this site, which is an alternative to more increasingly popular but highly parameterized and uncertain commercial ecohydrological models. It also provides a wider, novel context that is a prerequisite for any model‐based scenario assessment of likely impacts resulting from climate or land use change. Copyright © 2016 The Authors Hydrological Processes Published by John Wiley & Sons Ltd. Copyright © 2016 The Authors Hydrological Processes Published by 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.     

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.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Modified NLPM-ANN model and its application

The non-linear perturbation model based on artificial neural network (NLPM-ANN) takes advantage of the consideration of seasonal information by the linear perturbation model (LPM) and the notable non-linear simulation capability of artificial neural network (ANN). However, this model does not take account of antecedent catchment wetness that may effect the simulation and forecasting accuracy. A modified NLPM-ANN model is proposed and developed to take the consideration of antecedent catchment wetness. The output perturbing terms of the response function in the simple linear model (SLM) in an auxiliary component are taken as inputs of ANN to represent catchment wetness. The simulated total runoff is obtained by integrating the outputs of ANN with that of the seasonal model. The rainfall–runoff data of eight catchments were selected and used to compare the modified NLPM-ANN with the NLPM-ANN models. Results show that the modified NLPM-ANN is significantly superior to the NLPM-ANN, and the model component efficiency index values are 16.82% and 16.74% over the NLPM-ANN during calibration and verification periods, respectively.     

Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study

A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd.