Modelling evaporation using an artificial neural network algorithm

Sudheer, K. P.; Gosain, A. K.; Rangan, Dhruva; Saheb, Saeed

doi:10.1002/hyp.1096

Cited by 203 publications

(120 citation statements)

References 16 publications

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“…Although there are several different methods available for estimating potential ET (see Penman, 1948;Thornthwaite, 1948;Monteith, 1965;Priestley and Taylor, 1972;Hargreaves and Samani, 1985;Shuttleworth, 1992;Allen et al, 1998), or pan evaporation (e.g., data-driven approaches by Bruton et al, 2000;Sudheer et al, 2002;Jain and Roy, 2017), the estimation of actual ET is not straightforward. In practice, actual ET can be derived from model simulations, remotely sensed observations of different variables, etc.…”

Section: Statement Of the Problemmentioning

confidence: 99%

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Roy

Gupta

Serrat-Capdevila

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Daily, quasi-global (50 • N-S and 180 • W-E), satellite-based estimates of actual evapotranspiration at 0.25 • spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchmentscale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making "diagnostic structural improvements" to hydrologic models whenever possible.

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Section: Statement Of the Problemmentioning

confidence: 99%

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Roy

Gupta

Serrat-Capdevila

et al. 2017

Hydrol. Earth Syst. Sci.

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show abstract

“…Another important reason for data normalization is that different data sets represent observed values in different units. The similarity effect of data was also eliminated [73,74]. Figure 2 shows the influence of the number of hidden nodes on the performance evaluation criteria (NS, RMSE, MAE, and APE) for three training algorithms during the test period.…”

Section: Selection Of Optimal Mlp Models For Estimating Areal Rainfallmentioning

confidence: 99%

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Kim

Singh

2015

Water

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Abstract:The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg-Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg-Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

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“…Raw RADAR information was not considered to be reliable enough in mountainous regions, or in case of localised rainfall (Sun et al, 2000), and would consequently be calibrated with rain gauges. Hence, RADAR information is continuously evolving; such rainfall information was, thus, not calculated using a stationary process, preventing then to capitalise on ancient events.…”

Section: Physically Based Modelsmentioning

confidence: 99%

“…For this purpose Coulibaly et al (2000) used early stopping (Sjöberg and Ljung, 1992) and Sudheer et al (2002) used cross-validation (Stone, 1974. Several studies, however, show a performance assessment not based on an independent dataset, which could be the cause of result quality overestimation.…”

Section: Neural Network Modelsmentioning

confidence: 99%

Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

Artigue¹,

Johannet²,

Estupina

et al. 2012

Nat. Hazards Earth Syst. Sci.

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Abstract. In southern France, flash flood episodes frequently cause fatalities and severe damage. In order to inform and warn populations, the French flood forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appuià la Prévision des Inondations) initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital Basin) project in an effort to enhance flash flood predictability. The target area for this study is the Gardon d'Anduze basin, located in the heart of the Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be very high, resulting in flash flooding. Discharge and rainfall gauges are often exposed to extreme weather conditions, which undermines measurement accuracy and continuity. Moreover, the processes governing rainfall-discharge relations are not well understood for these steeply-sloped and heterogeneous basins. In this context of inadequate information on both the forcing variables and process knowledge, neural networks are investigated due to their universal approximation and parsimony properties. We demonstrate herein that thanks to a rigorous variable and complexity selection, efficient forecasting of up to two-hour durations, without requiring rainfall forecasting as input, can be derived using the measured discharges available from a feedforward model. In the case of discharge gauge malfunction, in degraded mode, forecasting may result using a recurrent neural network model. We also observe that neural network models exhibit low sensitivity to uncertainty in rainfall measurements since producing ensemble forecasting does not significantly affect forecasting quality. In providing good results, this study suggests close consideration of our main purpose: generating forecasting on ungauged basins.

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Modelling evaporation using an artificial neural network algorithm

Cited by 203 publications

References 16 publications

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

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