Explaining the internal behaviour of artificial neural network river flow models

Sudheer, K. P.; Jain, Ashu

doi:10.1002/hyp.5517

Cited by 112 publications

(55 citation statements)

References 28 publications

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“…There is no need for the modeller in this case to fully define the intermediate relationships (physical processes) between catchment descriptors and flood event magnitudes -the ANN identifies these during the "learning process". However, future work may involve 'drilling' into network models to extract and interrogate such relationships (e.g., Wilby et al (2003), Jain et al (2004) and Sudheer and Jain (2004)) -something that is beyond the scope of the current paper.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Flood estimation at ungauged sites using artificial neural networks

Dawson

Abrahart

Shamseldin

et al. 2006

Journal of Hydrology

224

111

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Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists.Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance. KeywordsArtificial neural networks, flood estimation, ungauged catchments Page 2 / 39 IntroductionThe UK Flood Estimation Handbook (FEH) notes that "many flood estimation problems arise at ungauged sites for which there are no flood peak data" (Reed and Robson, 1999:12 Pirt, 1983;Post and Jakeman, 1996;Sefton and Howarth, 1998). Page 3 / 39The FEH involves the use of an index flood procedure to derive the flood frequency curve at ungauged sites. The index flood is a middle-sized flood for which the mean or median of the flood data series is typically used (Grover et al., 2002) Hall et al. (2000) used between four and twelve input catchment characteristics to predict the same two EV1 parameter outputs using data from sites in Sumatra and Java; whereas Dastorani and Wright (2001) found that seven catchment inputs were sufficient to predict the index flood for selected catchments in the UK. This paper discusses the application of ANNs to predict the index flood for a much larger sample of selected catchments in the UK. It also considers the estimation of 10-, 20-and 30-year flood event magnitudes at such sites. Given the range of record lengths available, the 20-year flood event was chosen for further discussion as it is a convenient metric that is often used for the purposes of comparison in other studies (for example, see Reynard et al., 2004).

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Section: Artificial Neural Networkmentioning

confidence: 99%

Flood estimation at ungauged sites using artificial neural networks

Dawson

Abrahart

Shamseldin

et al. 2006

Journal of Hydrology

224

111

View full text Add to dashboard Cite

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“…Artificial neural networks (ANN) have been recently accepted as an efficient alternative tool for modeling of complex hydrologic system to the conventional methods and widely used for prediction. Some specific applications of ANN to hydrology include modeling rainfallrunoff process (Sajikumar et al, 1999), river flow forecasting (Dibike et al, 2001;Chang et al, 2002;Sudheer and Jain;Dawson et al, 2002), sediment transport prediction (Firat and Güngör, 2004), and sediment concentration estimation (Nagy et al, 2002). The ASCE Task Committee reports (2000) did a comprehensive review of the applications of ANN in hydrological forecasting context.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for river flow forecasting

Fırat

2008

Hydrol. Earth Syst. Sci.

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Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

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“…Sudheer [97] performed perturbation analysis to assess the influence of each individual input variable on the output variable and found it to be an effective means of identifying the underlying physical process inherent in the trained ANN. Olden et al [89], Sudheer and Jain [98], and Kingston et al [99] also addressed this issue of model transparency and knowledge extraction.…”

Section: Model Transparency and Knowledge Extractionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

121

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Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

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Explaining the internal behaviour of artificial neural network river flow models

Cited by 112 publications

References 28 publications

Flood estimation at ungauged sites using artificial neural networks

Flood estimation at ungauged sites using artificial neural networks

Comparison of Artificial Intelligence Techniques for river flow forecasting

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

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