Use of satellite communication of hydrometric data for the whole of Brazil

Abstract

The difficulties of exploiting the huge Brazilian hydrometeorological network led hydrologists of the Departamento Nacional de Águas e Energie Elétrica (DNAEE—Brazilian Department of Water and Electric Power) to use real time satellite telecommunications to improve network management. Data-gathering satellites, which are perfectly adapted to this network, were first used in 1975 and have interested DNAEE since 1980. Collaboration with ORSTOM permitted an initial evaluation of the technique in 1982. In 1984 DNAEE, together with ORSTOM, was able to set up a 20-station network in the Amazon. The results have been so encouraging that a scheme for 200 stations equipped with telecommunications facilities which use the Brazilian Satélite de Colecta de Dados number 1 (SCD1) satellite is in the final stages. This network will cover the entire country.     

Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin,India

ABSTRACT

Discharges and water levels are essential components of river hydrodynamics. In unreachable terrains and ungauged locations, it is quite difficult to measure these parameters due to rugged topography. In the present study an artificial neural network model has been developed for the Ramganga River catchment of the Ganga Basin. The modelled network is trained, validated and tested using daily water flow and level data pertaining to 4 years (2010–2013). The network has been optimized using an enumeration technique and a network topology of 4-10-2 with a learning rate set at 0.06, which was found optimum for predicting discharge and water-level values for the considered river. The mean square error values obtained for discharge and water level for the tested data were found to be 0.046 and 0.012, respectively. Thus, monsoon flow patterns can be estimated with an accuracy of about 93.42%.

Editor M.C. Acreman; Associate editor E. Gargouri     

Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment,Terengganu, Malaysia

ABSTRACT

This paper presents a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger     

An insight into the dynamics of Lake Nainital (Kumaun Himalaya,India) using stable isotope data / Un aperçu de la dynamique du Lac Nainital (Himalaya Kumaun,Inde) à partir de données isotopiques stables

Abstract

Abstract The water balance of Lake Nainital in the Kumaun Himalaya, India was previously computed using water budgeting and other indirect methods. An important data set of stable oxygen and hydrogen isotopic composition of water sources of the lake region was also presented and used to verify the annual estimates of subsurface flow/water balance. In the present study, the same data set has been used to investigate the dynamics of this lake in terms of the seasonal processes operative during the annual hydrological cycle: increased inflow during the monsoon, delayed groundwater inflow, and stratification and mixing of water. Based on the available data, a simple two-box model was used to constrain the values of exchange coefficients between the hypolimnion and epilimnion layers and to estimate evaporation and outflow components from the isotopic data.     

Ranking of river basin alternatives using ELECTRE

Abstract

Ranking of river basin planning and development alternatives under a multi-criterion environment, including both qualitative and quantitative aspects, is examined. The purpose is to find the most suitable planning for reservoirs with their associated purposes aimed at the development of the major peninsular river (Krishna) basin in India. A total of seven reservoirs and a diversion network are considered for the formulation of 24 alternative systems with 18 criteria, of which nine are qualitative and the remainder are quantitative in nature. A set of best alternatives with their ordering is obtained using ELECTRE (ELimination Et (and) Choice Translating REality).     

A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows

Abstract

A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Wei, S., Yang, H., Song, J.X., Abbaspour, K., and Xu, Z.X., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389.     

A BIVARIATE-REGRESSION MODEL FOR ESTIMATING CHEMICAL COMPOSITION OF STREAMFLOW OR GROUNDWATER / Modèle de régression bivarié pour l'évaluation de la composition chimique de l'écoulement total ou des eaux souterraines

Abstract

Regressions between concentrations of major inorganic constituents and either specific conductance or streamflow discharge characterize chemical-quality conditions at a sampling site or of an area and can be used to estimate streamflow chemical-quality composition over time and in space where information is lacking or deficient. In this manner, less costly water quality network operations may be achieved for a given programme, enabling available resources to be reallocated to the collection and analysis of data where information is deficient.

The SYSLAB system is a sequential set of documented special-purpose computer programs for statistically and graphically analysing historical water-quality records and deriving relevant regression relationships based upon this analysis. These computer programs have been applied under a variety of hydrological conditions to characterize regional chemical-quality patterns across the United States and in Pakistan. Case-study results using the proposed methodology are presented for Nigeria, Japan, and Pakistan.     

Municipal water demand forecasting under peculiar fluctuations in population: a case study of Mashhad,a tourist city

ABSTRACT

Forecasting future water demands has always been of great complexity, especially in the case of tourist cities which are subject to population fluctuations. In addition to the usual uncertainties related to climate and weather variables, daily water consumption in Mashhad, a tourist city is affected by a significant different fluctuation. Mashhad is the second most populous city in Iran. The number of tourists visiting the city is subject to national and religious events, which are respectively based on the Iranian formal calendar (secular calendar) and the Arabic Hijri calendar (Islamic religious calendar). Since religious events move relative to the secular calendar, the coincidence of the two calendars results in peculiar wild fluctuations in population. Artificial neural networks (ANNs) are chosen to predict water demand under such conditions. Three types of ANNs, feedforward back-propagation, cascade-forward and radial basis functions, are developed. In order to track how population fluctuation propagates in the model and affects the outputs, two sets of inputs are considered. For the first set, based on evaluating several repetitions, a typical combination of variables is selected as inputs, whereas for the second set, new calendar-based variables are included to decrease the effect of population fluctuations; the results are then compared using some performance criteria. A large number of runs are also conducted to assess the impact of random initialization of the weights and biases of networks and also the effect of calendar-based inputs on improvement of network performance. It is shown that, from the points of view of performance measures and unchanging outputs through numerous runs, the radial basis network that is trained by patterns including calendar-based inputs can provide the best domestic water demand forecasting under population fluctuations.

Editor D. Koutsoyiannis Associate editor E. Rozos     

Metamodel-based design of alluvial channels at incipient motion subjected to seepage

Abstract

The design of an alluvial channel affected by seepage requires information about five basic parameters: particle size, water depth, energy slope, seepage velocity, and average velocity. The conventional approach to predicting the incipient motion in an alluvial channel cannot be applied in the case of a channel affected by seepage. Metamodelling techniques are nowadays widely used in engineering design to simulate a complex system. Here, a metamodel is described which employs the radial-basis function (RBF) network to predict the seepage velocity and energy slope based on experimental data under incipient motion conditions. It was found that the model fits experimental data very well and provides predictions for the design. With the help of the metamodel generated by the RBF network, design curves based on the RBF metamodel are presented for use in designing an alluvial channel when it is affected by seepage.

Citation Kumar, B., Sreenivasulu, G. & Ramakrishna Rao, A. (2010) Metamodel-based design of alluvial channels at incipient motion subjected to seepage. Hydrol. Sci. J. 55(3), 459–466.     

Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm / Modélisation d’incertitudes dans la gestion de barrage à court terme grâce à des ensembles flous et à un algorithme génétique

Abstract

Abstract Various uncertainties are inherent in modelling any reservoir operation problem. Two of these are addressed in this study: uncertainty involved in the expression of reservoir penalty functions, and uncertainty in determining the target release value. Fuzzy set theory was used to model these uncertainties where the preferences of the decision maker for the fuzzified parameters are expressed as membership functions. Nonlinear penalty functions are used to determine the penalties due to deviations from targets. The optimization was performed using a genetic algorithm with the objectives to minimize the total penalty and to maximize the level of satisfaction of the decision maker with fuzzified input parameters. The proposed formulation was applied to the problem of finding the optimal release and storage values, taking Green reservoir in Kentucky, USA as a case study. The approach offers more flexibility to reservoir decision-making by demonstrating an efficient way to represent subjective uncertainties, and to deal with non-commensurate objectives under a fuzzy multi-objective environment.     

A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial

Abstract

Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance.     

Comparison of SMOS,modelled and in situ long-term soil moisture series in the northwest of Spain

ABSTRACT

This work aimed to evaluate the capability of modelled vs in situ soil moisture observations in the northwest of Spain for a period of four years (2010–2013) in order to validate the SMOS L2 product. Comparisons were performed for a set of representative stations of the Soil Moisture Measurement Stations network of the University of Salamanca (REMEDHUS) at both point and area scales. The SMOS series showed good correlation with the modelled series, better than that obtained with the in situ observations (0.77 vs 0.68 average correlation coefficients). However, some underestimation or overestimation of the SMOS series, related to the soil characteristics, was observed with respect to both the in situ and the modelled series. The SMOS data normalization produced a notable improvement in the results, highlighting the capability of the modelled data to validate the SMOS soil moisture series. This research provides a solid foundation for the future validation of SMOS at large scales, overcoming the spatial representativeness issues arising from the use of in situ point measurements.

Editor M.C. Acreman; Associate editor N. Verhoest     

Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

ABSTRACT

Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Catchment-scale storm velocity: quantification,scale dependence and effect on flood response

Abstract

The concept of “catchment-scale storm velocity” quantifies the rate of storm motion up and down the basin accounting for the interaction between the rainfall space–time variability and the structure of the drainage network. It provides an assessment of the impact of storm motion on flood shape. We evaluate the catchment-scale storm velocity for the 29 August 2003 extreme storm that occurred on the 700 km²-wide Fella River basin in the eastern Italian Alps. The storm was characterized by the high rate of motion of convective cells across the basin. Analysis is carried out for a set of basins that range in area from 8 to 623 km² to: (a) determine velocity magnitudes for different sub-basins; (b) examine the relationship of velocity with basin scale and (c) assess the impact of storm motion on simulated flood response. Two spatially distributed hydrological models of varying degree of complexity in the representation of the runoff generation processes are used to evaluate the effects of the storm velocity on flood modelling and investigate model dependencies of the results. It is shown that catchment-scale storm velocity has a non-linear dependence on basin scale and generally exhibits rather moderate values, in spite of the strong kinematic characteristics of individual storm elements. Consistently with these observations and for both models, hydrological simulations show that storm motion has an almost negligible effect on the flood response modelling.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore

Citation Nikolopoulos, E.I., Borga, M., Zoccatelli, D., and Anagnostou, E.N., 2014. Catchment-scale storm velocity: quantification, scale dependence and effect on flood response. Hydrological Sciences Journal, 59 (7), 1363–1376. http://dx.doi.org/10.1080/02626667.2014.923889     

Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt

Abstract

Abstract The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and down-stream station sediment data, separately, and the second part focuses on the estimation of downstream suspended sediment data by using data from both stations. In each case, the MLP test results are compared to those of generalized regression neural networks (GRNN), radial basis function (RBF) and multi-linear regression (MLR) for the best-input combinations. Based on the comparisons, it was found that the MLP generally gives better suspended sediment concentration estimates than the other neural network techniques and the conventional statistical method (MLR). However, for the estimation of maximum sediment peak, the RBF was mostly found to be better than the MLP and the other techniques. The results also indicate that the RBF and GRNN may provide better performance than the MLP in the estimation of the total sediment load.     

Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization

Abstract

Artificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.

Editor Z.W. Kundzewicz; Associate editor L. See

Citation Tapoglou, E., Trichakis, I.C., Dokou, Z., Nikolos, I.K., and Karatzas, G.P., 2014. Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization. Hydrological Sciences Journal, 59(6), 1225–1239. http://dx.doi.org/10.1080/02626667.2013.838005     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

Streamflow forecasting using least-squares support vector machines

Abstract

This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.

Editor D. Koutsoyiannis; Associate editor L. See

Citation Shabri, A. and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275–1293.     

Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events

Abstract

This paper describes a fuzzy rule-based approach applied for reconstruction of missing precipitation events. The working rules are formulated from a set of past observations using an adaptive algorithm. A case study is carried out using the data from three precipitation stations in northern Italy. The study evaluates the performance of this approach compared with an artificial neural network and a traditional statistical approach. The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions. The problems that have yet to be addressed are overfitting and applicability outside the range of training data.     

Coding random self-similar river networks and calculating geometric distances: 2. Application to runoff simulations / Codage de réseaux hydrographiques auto-similaires aléatoires et calcul de distances géométriques: 2. Application à la simulation de l'écoulement

Abstract

A preliminary method for coding random self-similar river networks and the corresponding distance calculations are proposed in a companion paper. The coding method is applied to generate random self-similar river networks, and the corresponding algorithm for calculating the geometric distances of the links is employed to determine the width function of the river networks, and thus evaluates the adaptability of the process. The width function-based geomorphological instantaneous unit hydrograph (WF-GIUH) model is then applied to estimate the runoff of the Po-bridge watershed in northern Taiwan. The results imply that the separately random self-similar generating algorithm can be used to simulate river networks during the rainfall–runoff process. It can also help analyse the variations of the river network when rainfall locations change and study the influence on hydrological responses (IUH) when the shape of river network changes.