Application of Artificial Neural Networks in Forecasting a Standardized Precipitation Evapotranspiration Index for the Upper Blue Nile Basin

Mulualem, Getachew; Liou, Yuei An

doi:10.3390/w12030643

Cited by 53 publications

(38 citation statements)

References 62 publications

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“…Although the readjusted hydrological images were used for the training and testing of the CNN model and the data were used for training, verification, and testing of the model, there is no standard set for the data ratio. However, training and verification data were used in the ratio of 7:3 or 8:2 in several previous studies [57][58][59][60]. Therefore, the data were divided as shown in Table 4, and the model was divided into Cases 1, 2, and 3, used herein for training, verification, and testing, respectively.…”

Section: Results Of Building the Hydrological Imagementioning

confidence: 99%

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Kim

Song²

2020

Water

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This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.

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Section: Results Of Building the Hydrological Imagementioning

confidence: 99%

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Kim

Song²

2020

Water

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“…(2008); Barua et al . (2012); Mishra and Desai (2006); and Mulualem and Liou (2020) that highlighted the efficiency of NNs in predicting the drought events accurately. The results from this research exhibited that data integration could utilize the robustness of discrete SPPs through merging data from multiple sources to derive highly accurate estimations.…”

Section: Discussionmentioning

confidence: 97%

Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

Kolluru

2021

Intl Journal of Climatology

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Accurate estimation and prediction of drought events are highly essential for implementing effective planning and management strategies to handle this complex natural phenomenon. Application of machine learning algorithms (MLAs) for integrating satellite precipitation products (SPPs), unlike gauge observations, can furnish precise drought estimations. In this study, we have proposed and tested two approaches (pre and post‐integration of SPPs) that deal with the prediction of drought employing 13 MLAs. Three SPPs are integrated under four combinations (involves two and three datasets integration) employing pre and post integration approaches to predict Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index at various temporal scales (1, 3, 6 and 12‐month). From the overall results, Approach‐2 that involves estimation of drought before integration using MLAs proved effective than Approach‐1 (prediction of drought post‐integration). Neural Networks based Bayesian Regularization (NBR) under three dataset integration outperformed at all temporal scales and climatic zones of India when compared to the other 12 MLAs and two dataset integration combinations. The blended product (NBR) manifested enhancements in statistical results at all temporal scales and climatic zones. European Centre for Medium‐Range Weather Forecasts ReAnalysis (ERA‐5) dataset performed better in predicting drought events in more climatic zones compared to Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) when compared against Indian Meteorological Department (IMD) dataset. In contrast, PERSIANN‐CDR proved effective in predicting drought at the country scale. ERA‐5 could be suitable for real‐time drought monitoring and prediction, whereas PERSIANN‐CDR can be used for retrospective drought analysis. The proposed approach and the best‐performed algorithm (NBR) can be extended and applied in any climatic region for enhancing the drought predictions where remotely sensed information are accessible even in regions with finite ground data availability.

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“…Therefore, the developed TLRNN model for the next three days shows a satisfactory prognostic ability. Similarly, Mulualem and Liou [38] applied seven ANN models incorporating hydrometeorological, climate, sea surface temperatures, and topographic data for crop ET estimate in in the Upper Blue Nile basin of Ethiopia. They reported that coefficient of determination and the root-meansquare error of the best architecture ranged from 0.820 to 0.949 and 0.263 to 0.428, respectively.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Forecasting Reference Evapotranspiration Using Time Lagged Recurrent Neural Network

Proias

Gravalos

Papageorgiou

et al. 2020

Wseas Transactions on Environment and Development

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The aim of this study is to employ a Time Lagged Recurrent Neural Network (TLRNN) model for forecasting near future reference evapotranspiration (ETo) values by using climate data taken from meteorological station located in Velestino, a village near the city of Volos, in Thessaly, centre of Greece. TLRNN is Multilayer Perceptron Neural Network (MLP-NN) with locally recurrent connections and short-term memory structures that can learn temporal variations from the dataset. The network topology is using input layer, hidden layer and a single output with the ETo values. The network model was trained using the back propagation through time algorithm. Performance evaluations of the network model done by comparing the Mean Bias Error (MBE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Index of Agreement (IA). The evaluation of the results showed that the developed TLRNN model works properly and the forecasting ETo values approximate the FAO-56 PM values. A good proximity of predictions with the experimental data was noticed, achieving coefficients of determination (R2) greater than 75% and root mean square error (RMSE) values less than 1.0 mm/day. The forecasts range up to three days ahead and can be helpful to farmers for irrigation scheduling.

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Application of Artificial Neural Networks in Forecasting a Standardized Precipitation Evapotranspiration Index for the Upper Blue Nile Basin

Cited by 53 publications

References 62 publications

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

Forecasting Reference Evapotranspiration Using Time Lagged Recurrent Neural Network

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