An artificial neural network-based rainfall runoff model for improved drainage network modelling

This Presentation is brought to you for free and open access by the City College of New York at CUNY Academic Works. It has been accepted for inclusion in International Conference on Hydroinformatics by an authorized administrator of CUNY Academic Works. For more information, please contact [email protected] th International Conference on Hydroinformatics HIC 2014, New York City, USAModelling rainfall-runoff processes enables hydrologists to plan their response to flooding events. Urban drainage catchment modelling requires rainfall-runoff models as a prerequisite. In the UK, one of the main software tools used for drainage modelling is InfoWorks CS, based on relatively simple methods which are relatively robust in predicting runoff. This paper presents an alternative approach to modelling runoff that will allow for the complex inter-relation of runoff that occurs from impermeable areas, permeable areas, local surface storage and variation in rainfall induced infiltration. Apart from the uncertainties associated with the measurement of connected surfaces to the drainage system, the physical processes involved in runoff are nonlinear, making artificial neural networks (ANNs) an ideal candidate for modelling them. ANNs have been used for runoff prediction in natural catchments, and recently on a study for predicting the performance of urban drainage systems. This study seeks to determine an input set that predicts sewerage flow in urban catchments where the runoff is dominated by infiltration, a major issue for the water industry. A framework is proposed in which an ANN is trained by an evolutionary algorithm, which optimises ANN weights; results are assessed using the Nash-Sutcliffe Efficiency Coefficient. The model is demonstrated on a real-world case study site for which rainfall, flow, air temperature and groundwater levels in three boreholes have been measured. Various combinations of these data are used as model inputs, examining a mixture of daily and sub-daily timesteps. The best predictions are generated from daily linearly combined antecedent rainfall and air temperature, although sub-daily information improves the worst-case performance of the model. Although infiltration is affected by groundwater levels, incorporating groundwater into the model does not improve predictions. The proposed ANN model is capable of producing acceptable predictions, thus avoiding many of the uncertainties involved in traditional infiltration modelling

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Runoff is one of the most important components of the hydrological cycle, and having complete series of runoff data is essential for any hydrological modelling process. This study aims to estimate the runoff at Al-Jawadiyah hydrometric station using artificial neural networks. This study used only the runoff data at Al-Jawadiyah station in addition to the runoff values measured at Al-Amiri station on the Syrian-Lebanese border. Many experiments were conducted and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons and the training algorithms until the best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values, and the network (2:12:1) was adopted in the process of filling the gaps in the runoff time series at Al-Jawadiyah station during the study period. This study recommends working on preparing complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area

Wavelet cross-correlation analysis of wind speed series generated by ANN based models

International audienceTo obtain, over medium term periods, wind speed time series on a site, located in the southern part of the Paris region (France), where long recording are not available, but where nearby meteorological stations provide large series of data, use was made of ANN based models. The performance of these models have been evaluated by using several commonly used statistics such as average absolute error, root mean square error, normalized mean square error, and correlation coefficient. Such global criteria are good indicators of the "robustness" of the models but are unable to provide useful information about their "effectiveness" in accurately generating wind speed fluctuations over a wide range of scales. Therefore a complementary wavelet cross coherence analysis has been performed. Wavelet cross coherence, wavelet cross-correlation and spectral wavelet cross-correlation coefficients, have been calculated and displayed as functions of the equivalent Fourier period. These coefficients provide quantitative measures of the scale-dependence of the model performance. In particular the spectral wavelet cross coherence coefficient can be used to have a rapid and efficient identification of the validity range of the models. The results show that the ANN models employed in this study are only effective in computing large-scale fluctuations of large amplitude. To obtain a more representative time series, with much higher resolution, small-scale fluctuations have to be simulated by a superimposed statistical model. By combining ANN and statistical models, both the high and the low-frequency segments of the wind velocity spectra can be simulated, over a range of several hours, at the target site.Pour générer des signaux synthétiques représentatifs du vent, sur un site situé dans le sud de la région parisienne (France) où très peu de données étaient disponibles, nous avons utilisé des données météo enregistrées dans des stations météorologiques voisines et des modèles basés sur des réseaux de neurones artificiels (RNA). Les performances de tels modèles sont généralement évaluées à l'aide d’indicateurs statistiques, tels que l'erreur absolue moyenne, l'erreur quadratique moyenne, l'erreur quadratique moyenne normalisée et le coefficient de corrélation. Ces indicateurs globaux servent à mesurer la « robustesse » des modèles, mais ils ne permettent pas, à priori, de mesurer leur aptitude à restituer l’ensemble des échelles contenues dans un signal multi-échelles. Aussi, nous avons proposé de mesurer leur efficacité à l’aide des propriétés temps-échelles des transformées en ondelettes continues. Pour cela, nous avons calculé, à différentes échelles, les fonctions de corrélation croisée en ondelettes, de cohérence croisée en ondelettes et les coefficients de corrélation croisée en ondelettes. Ces coefficients fournissent des mesures quantitatives, à chaque échelle, de la performance du modèle. Ils permettent, en particulier, de définir rapidement et efficacement la gamme de fluctuation que le modèle est apte à restituer. Les résultats ont montré que les modèles RNA utilisés dans cette étude ne reconstruisent correctement que les grandes échelles du vent, qui correspondent aux fluctuations lentes. Pour reconstruire les fluctuations turbulentes, rapides, un modèle classique de génération de processus stochastique a été utilisé. Ainsi, en combinant les deux types de modèles, sur le site considéré, toutes les gammes de fluctuation ont pu être simulées, sur des périodes de plusieurs heures

An Artificial Neural Network-Based Rainfall Runoff Model For Improved Drainage Network Modelling

Modelling rainfall-runoff processes enables hydrologists to plan their response to flooding events. Urban drainage catchment modelling requires rainfall-runoff models as a prerequisite. In the UK, one of the main software tools used for drainage modelling is InfoWorks CS, based on relatively simple methods which are relatively robust in predicting runoff. This paper presents an alternative approach to modelling runoff that will allow for the complex inter-relation of runoff that occurs from impermeable areas, permeable areas, local surface storage and variation in rainfall induced infiltration. Apart from the uncertainties associated with the measurement of connected surfaces to the drainage system, the physical processes involved in runoff are nonlinear, making artificial neural networks (ANNs) an ideal candidate for modelling them. ANNs have been used for runoff prediction in natural catchments, and recently on a study for predicting the performance of urban drainage systems. This study seeks to determine an input set that predicts sewerage flow in urban catchments where the runoff is dominated by infiltration, a major issue for the water industry. A framework is proposed in which an ANN is trained by an evolutionary algorithm, which optimises ANN weights; results are assessed using the Nash-Sutcliffe Efficiency Coefficient. The model is demonstrated on a real-world case study site for which rainfall, flow, air temperature and groundwater levels in three boreholes have been measured. Various combinations of these data are used as model inputs, examining a mixture of daily and sub-daily timesteps. The best predictions are generated from daily linearly combined antecedent rainfall and air temperature, although sub-daily information improves the worst-case performance of the model. Although infiltration is affected by groundwater levels, incorporating groundwater into the model does not improve predictions. The proposed ANN model is capable of producing acceptable predictions, thus avoiding many of the uncertainties involved in traditional infiltration modelling

Verification of MIKE 11-NAM Model for runoff modeling using ANN, FIS, and ARIMA methods in poorly studied basin

Hydrological information is the basis for conducting water balance studies in any region, and surface runoff is one of the most important hydrological parameters and one of the most difficult in the process of estimation and prediction. This study aims to verification of the MIKE 11-NAM Model for runoff modeling using artificial neural network (ANN), fuzzy inference system (FIS), and autoregressive integrated moving average (ARIMA) methods at Al-Jawadiyah hydrometric station on the Orontes River in Syria. MATLAB was used to build neural and fuzzy models, where many models were built with the change in all parameters, functions, and algorithms that can be used, and the Minitab was used to build ARIMA models. Many models were prepared with the addition of seasonal effect, and the comparison results showed an advantage for artificial neural network models in terms of evaluation parameters. After that, the artificial neural network models were adopted in the process of filling the gaps in the time series of surface runoff in the study area to be used in the Mike program for modeling the runoff and through the method of trial and error with a high number of iterative cycles, model parameters were calculated and runoff values estimated. Still, the results were not good, and there were significant differences between the measured values and the values simulated by the model, and this is due to the significant lack of available data. This study recommends the use of artificial intelligence and machine learning models in the field of estimation and prediction of hydrological parameters

Constitutive models for the prediction of the hot deformation behavior of the 10%Cr steel alloy

The aim of this paper is to establish a reliable model that provides the best fit to the specific behavior of the flow stresses of the 10%Cr steel alloy at the time of hot deformation. Modified Johnson-Cook and strain-compensated Arrhenius-type (phenomenological models), in addition to two Artificial Neural Network (ANN) models were established with the view toward investigating their stress prediction performances. The ANN models were trained using Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) algorithms. The prediction accuracy of the established models was evaluated using the following well-known statistical parameters: (a) correlation coefficient (R), (b) Average Absolute Relative Error (AARE), (c) Root Mean Squared Error (RMSE), and Relative Error (RE). The results showed that both of the modified Johnson-Cook and strain-compensated Arrhenius models could not competently predict the flow behavior. On the contrary, the results indicated that the two proposed ANN models precisely predicted the flow stress values and that the LM-trained ANN provided a superior performance over the SCG-trained model, as it yielded an RMSE of as low as 0.441 MPa. - 2019 by the authors

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

O objetivo deste trabalho foi avaliar a densidade de amostragem na acurácia de predição de ordens de solos, com alta resolução espacial, em área vitícola da Serra Gaúcha. Para isso, utilizou-se modelo digital de elevação (MDE) do terreno, base cartográfica, mapa convencional de solos e o programa Idrisi. Sete variáveis preditoras foram calculadas e lidas junto com as classes de solo, em pontos aleatoriamente distribuídos, nas densidades de 0,5, 1, 1,5, 2 e 4 pontos por hectare. Os dados foram usados para treinar uma árvore de decisão (Gini) e três redes neurais artificiais: teoria da ressonância adaptativa, fuzzy ARTMap; mapa auto‑organizável, SOM; e perceptron de múltiplas camadas, MLP. Os mapas estimados foram comparados com o mapa de solos convencional para calcular erros de omissão e de inclusão, exatidão geral, e erros de quantidade e de alocação. A árvore de decisão foi menos sensível à densidade de amostragem e apresentou maior acurácia e consistência. O SOM foi a rede neural com menor sensibilidade e maior consistência. O MLP apresentou mínimo crítico e maior inconsistência, enquanto fuzzy ARTMap apresentou maior sensibilidade e menor acurácia. Os resultados indicam que densidades de amostragem usadas em levantamentos convencionais podem servir de referência para estimar ordens de solos na Serra Gaúcha.The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha