Modelling of river discharges using neural networks derived from support vector regression

Neural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls.published_or_final_versio

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Author name used in this publication: Chun-Tian Cheng2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Prediction of River Discharge by Using Gaussian Basis Function

For design of water resources engineering related project such as hydraulic structures like dam, barrage and weirs river discharge data is vital. However, prediction of river discharge is complicated by variations in geometry and boundary roughness. The conventional method of estimation of river discharge tends to be inaccurate because river discharge is nonlinear but the method is linear. Therefore, an alternative method to overcome problem to predict river discharge is required. Soft computing technique such as artificial neural network (ANN) was able to predict nonlinear parameter such as river discharge. In this study, prediction of river discharge in Pari River is predicted using soft computing technique, specifically gaussian basis function. Water level raw data from year 2011 to 2012 is used as input. The data divided into two section, training dataset and testing dataset. From 314 data, 200 are allocated as training data and the remaining 100 are used as testing data. After that, the data will be run by using Matlab software. Three input variables used in this study were current water level, 1-antecendent water level, and 2-antecendent water level. 19 numbers of hidden neurons with spread value of 0.69106 was the best choice which creates the best result for model architecture after numbers of trial. The output variable was river discharge. Performance evaluation measures such as root mean square error, mean absolute error, correlation of efficiency (CE) and coefficient of determination (R2) was used to indicate the overall performance of the selected network. R2 for training dataset was 0.983 which showed predicted discharge is highly correlated with observed discharge value. However, testing stage performance is decline from training stage as R2 obtained was 0.775 consequently presence of outliers have affect scattering of whole data of testing and resulted in less accuracy as the R2 obtained much lower compared to training dataset. This happened because less number of input loaded into testing than training. RMSE and MSE recorded for training much lower than testing indicated that the better the performance of the model since the error is lesser. The comparison of with other types of neural network showed that Gaussian basis function is recommended to be used for river discharge prediction in Pari river

Potential of support-vector regression for forecasting stream flow

Vodotok je važan za hidrološko proučavanje zato što određuje varijabilnost vode i magnitudu rijeke. Inženjerstvo vodnih resursa uvijek se bavi povijesnim podacima i pokušava procijeniti prognostičke podatke kako bi se osiguralo bolje predviđanje za primjenu kod bilo kojeg vodnog resursa, na pr. projektiranja vodnog potencijala brane hidroelektrana, procjene niskog protoka, i održavanja zalihe vode. U radu se predstavljaju tri računalna programa za primjenu kod rješavanja ovakvih sadržaja, tj. umjetne neuronske mreže - artificial neural networks (ANNs), prilagodljivi sustavi neuro-neizrazitog zaključivanja - adaptive-neuro-fuzzy inference systems (ANFISs), i support vector machines (SVMs). Za stvaranje procjene korištena je Rijeka Telom, smještena u Cameron Highlands distriktu Pahanga, Malaysia. Podaci o dnevnom prosječnom protoku rijeke Telom, kao što su količina padavina i podaci o vodostaju, koristili su se za period od ožujka 1984. do siječnja 2013. za podučavanje, ispitivanje i ocjenjivanje izabranih modela. SVM pristup je dao bolje rezultate nego ANFIS i ANNs kod procjenjivanja dnevne prosječne fluktuacije vodotoka.Stream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River’s daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984 – January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream’s flow

Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China

Flood prediction methods play an important role in providing early warnings to government offices. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied three different types of Artificial Neural Networks (ANN) and an autoregressive model to study the Jinsha river basin (JRB), in the upper part of the Yangtze River in China. The three ANN techniques include feedforward back propagation neural networks (FFBPNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBFNN). Artificial Neural Networks (ANN) has shown Great deal of accuracy as compared to statistical autoregressive (AR) model because statistical model cannot able to simulate the non-linear pattern. The results varied across the cases used in the study; based on available data and the study area, FFBPNN showed the best applicability, compared to other techniques

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrological insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrological context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrological soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log-log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrological interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log-log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product — irrespective of their poorer global goodness-of-fit statistics

River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

In this study, an extended version of variable parameter McCarthy–Muskingum (VPMM) method originally proposed by Perumal and Price (J Hydrol 502:89–102, 2013) was compared with the widely used data-based model, namely support vector machine (SVM) and hybrid wavelet-support vector machine (WASVM) to simulate the hourly discharge in Neckar River wherein significant lateral flow contribution by intermediate catchment rainfall prevails during flood wave movement. The discharge data from the year 1999 to 2002 have been used in this study. The extended VPMM method has been used to simulate 9 flood events of the year 2002, and later the results were compared with SVM and WASVM models. The analysis of statistical and graphical results suggests that the extended VPMM method was able to predict the flood wave movement better than the SVM and WASVM models. A model complexity analysis was also conducted which suggests that the two parameter-based extended VPMM method has less complexity than the three parameter-based SVM and WASVM model. Further, the model selection criteria also give the highest values for VPMM in 7 out of 9 flood events. The simulation of flood events suggested that both the approaches were able to capture the underlying physics and reproduced the target value close to the observed hydrograph. However, the VPMM models are slightly more efficient and accurate, than the SVM and WASVM model which are based only on the antecedent discharge data. The study captures the current trend in the flood forecasting studies and showed the importance of both the approaches (physical and data-based modeling). The analysis of the study suggested that these approaches complement each other and can be used in accurate yet less computational intensive flood forecasting

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling or hydrological climate-impact studies. This paper identifies major drivers of daily streamflow from large-scale atmospheric circulation using two reanalysis datasets for six catchments in Norway representing various Köppen-Geiger climate types and flood-generating processes. A nested loop of roughly pruned random forests is used for feature extraction, demonstrating the potential for automated retrieval of physically consistent and interpretable input variables. Random forest (RF), support vector machine (SVM) for regression and multilayer perceptron (MLP) neural networks are compared to multiple-linear regression to assess the role of model complexity in utilizing the identified major drivers to reconstruct streamflow. The machine learning models were trained on 31 years of aggregated atmospheric data with distinct moving windows for each catchment, reflecting catchment-specific forcing-response relationships between the atmosphere and the rivers. The results show that accuracy improves to some extent with model complexity. In all but the smallest, rainfall-driven catchment, the most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging from 0.71 to 0.81 on testing data spanning five years. The poorer performance by all models in the smallest catchment is discussed in relation to catchment characteristics, sub-grid topography and local variability. The intra-model differences are also viewed in relation to the consistency between the automatically retrieved feature selections from the two reanalysis datasets. This study provides a benchmark for future development of deep learning models for direct downscaling from large-scale atmospheric variables to daily streamflow in Norway.publishedVersio