Integration of artificial neural networks with conceptual models in rainfall-runoff modeling

Chen, Jieyun; Adams, Barry J.

doi:10.1016/j.jhydrol.2005.06.017

Cited by 115 publications

(43 citation statements)

References 25 publications

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“…In this study, the Genetic Algorithm (Wang, 1991) is used for optimising the XAJ model's 16 parameters. Upon completing the automatic parameter optimisation, minor adjustments of the model parameter values (i.e., EX, B, WUM, WLM and WDM) are made by the trial and error method (Chen and Adams, 2006). The calibration procedure focuses especially on the modelling of the actual evapotranspiration and the partition of total runoff (i.e., surface runoff, interflow and groundwater) based on a good agreement between the estimated and the observed flow.…”

Section: Xaj Flow Simulation Resultsmentioning

confidence: 99%

Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

Zhuo

Han

2016

Journal of Hydrology

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Although many conceptual models are very effective in simulating river runoff, their soil moisture schemes are generally not realistic in comparison with the reality (i.e., getting the right answers for the wrong reasons). This study reveals two significant misrepresentations in those models through a case study using the Xinanjiang model which is representative of many well-known conceptual hydrological models. The first is the setting of the upper limit of its soil moisture at the field capacity, due to the 'holding excess runoff' concept (i.e., runoff begins on repletion of its storage to the field capacity). The second is neglect of capillary rise of water movement. A new scheme is therefore proposed to overcome those two issues. The amended model is as effective as its original form in flow modelling, but represents more logically realistic soil water processes. The purpose of the study is to enable the hydrological model to get the right answers for the right reasons. Therefore, the new model structure has a better capability in potentially assimilating soil moisture observations to enhance its real-time flood

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Section: Xaj Flow Simulation Resultsmentioning

confidence: 99%

Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

Zhuo

Han

2016

Journal of Hydrology

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“…Where, * X is the average value of observed (or experimental) data and * X is the average value of predicted data [15]. The model with high CC, less MAE and better MEF is considered as best suited amongst ANN and REG approaches adopted in prediction of the variables used for evaluation of hydrodynamic performance of QBW.…”

Section: Model Performance Analysismentioning

confidence: 99%

Comparison of Artificial Neural Network and Regression Approaches for Evaluation of Hydrodynamic Performance of Quarter Circular Break Water

Vivekanandan¹

2018

Int J Adv Tech

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“…(7) where X is the average value of observed rainfall and * X is the average value of predicted rainfall [15].…”

Section: B) Multi-layer Perceptron Networkmentioning

confidence: 99%

Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall

Vivekanandan¹

2017

IJIRCST

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Abstract-Prediction of rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support. Further, such data need to be analyzed in different ways, depending on the issue under consideration. In the present study, Extreme Value Type-1 (EV1) distribution based on statistical approach and Multi Layer Perceptron (MLP) network based on Artificial Neural Network (ANN) is adopted for prediction of rainfall at Fatehabad and Hansi. The performance of the statistical and ANN approaches used in rainfall predication are evaluated by model performance indicators viz., correlation coefficient, model efficiency and mean absolute percentage error. The study shows the MLP is found to be better suited network for prediction of rainfall at Fatehabad whereas EV1 for Hansi. Index Terms-Artificial I. INTRODUCTIONPrediction of rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support [1]. Further, such data need to be analyzed in different ways, depending on the issue under consideration. Out of a number of probability distributions, the family of Extreme Value Distributions (EVDs) includes Generalized Extreme Value, Extreme Value Type-1 (EV1), Extreme Value Type-2 and Extreme Value Type-3 is generally used for rainfall prediction. EVDs arise as limiting distributions for the sample of independent, identically distributed random variables, as the sample size increases. In the group of EVDs, EV1 distribution has no shape parameter as when compared to other distributions and this means that there is no change in the shape of Probability Distribution Function (PDF). Moreover, EV1 distribution has the advantage of having only positive values, since the data series of rainfall are always positive; and therefore EV1 distribution is important in statistics. Apart from this, with the development of Artificial Intelligence (AI), a number of various AI methods have been developed for prediction of rainfall. The new developed methods include Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System, Fuzzy Logic, Support Vector Machine, Neuro-Fuzzy Network and Evolutionary Optimization Algorithm. Out of these methods, ANN could deal with non-linear and complex problems in terms of classification or forecasting. The ANN models can represent a complex nonlinear relationship and extract the dependence between variables through the training process [2][3]. In the present study, statistical and ANN approaches are adopted for prediction of rainfall for the da...

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Integration of artificial neural networks with conceptual models in rainfall-runoff modeling

Cited by 115 publications

References 25 publications

Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

Comparison of Artificial Neural Network and Regression Approaches for Evaluation of Hydrodynamic Performance of Quarter Circular Break Water

Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall

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