Comparison of deep neural networks for reference evapotranspiration prediction using minimal meteorological data

Sowmya, M. R.; Kumar, Mukesh; Ambat, Sooraj K.

doi:10.1109/accthpa49271.2020.9213201

Cited by 12 publications

(8 citation statements)

References 36 publications

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“…The DNN learning mechanism involves the iterative execution of feed-forward and error back-propagation cycles until the optimal degree of precision has been attained (Saggi and Jain 2019;Sowmya et al 2020). DNN's generalization efficiency relies on the activation function used.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…The back-propagation error mechanism reinforces the model through a derivative method of error calculation and weight and bias updation. The weights of each neuron are adjusted with a loss function, and the loss function has to be minimized during the training process to increase model performance (Sowmya et al 2020;Vieira et al 2020). For regression, the widely used loss function is mean squared error (MSE) and it is computed using Eq.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…In addition to the factors mentioned above, such as hidden layer counts, neuron counts in hidden layers, activation functions, optimization, certain other factors are also required to describe the DNN architecture, such as momentum, dropout, learning rate, regularisation, and epoch size (Sowmya et al 2020;Vieira et al 2020).…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Minimal input parameter-based ETo modelling research has acquired wide recognition in hydro-meteorological communities, especially in developing nations where weather stations and data collection methods are few (Adamala et al 2015;Debnath et al 2015;Roy 2021). Among these, certain recent studies have implemented the AI techniques such as artificial neural networks (ANN) and their derivatives (Ferreira et al 2019;Reis et al 2019;Ferreira and da Cunha 2020a;Sowmya et al 2020;Bellido-Jiménez et al 2021;Kaya et al 2021), adaptive neuro fuzzy inference systems (ANFIS) (Petković et al 2020;Üneş et al 2020), support vector machine (SVM) and their hybrid variants (Chia et al 2020a;Tikhamarine et al 2020;Ahmadi et al 2021), tree based soft computing methods (Fan et al 2018;Wu et al 2020), gene expression programming (GEP) (Kazemi et al 2021;Muhammad et al 2021), hybrid ML models (Mohammadi and Mehdizadeh 2020;Zhu et al 2020;Kisi et al 2021;Gong et al 2021), and ensemble of ML models (Wu et al 2021;Martín et al 2021) for the ETo modelling. Most of these studies have reported success by experimenting with different input parameters for modelling, such as only temperature (Bellido-Jiménez et al 2021), a combination of temperature and solar radiation (Chia et al 2020a), temperature and relative humidity (Ferreira and da Cunha 2020a), temperature and wind speed (Nagappan et al 2020) or a variety of other combinations to minimize meteorological data usage.…”

Section: Introductionmentioning

confidence: 99%

“…A reduced feature one-dimensional convolution neural network (CNN) model for ETo prediction was developed by Nagappan et al (2020) and succeeded with promising results. Sowmya et al (2020) developed various DNN variants for ETo prediction using different feature combinations, and analyzed their predictive performances. Both local and regional based ETo forecasting studies (Ferreira and da Cunha 2020a, b), and ETo time-series forecasting study (de Oliveira e Lucas et al 2020) demonstrated the strength of CNN and its variants.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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Section: Deep Neural Networkmentioning

confidence: 99%

Section: Deep Neural Networkmentioning

confidence: 99%

Section: Deep Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

Sowmya

Kumar

Ambat

et al. 2022

Hydrological Processes

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An accurate and minimal meteorological parameter‐based reference evapotranspiration (ETo) computational model is highly essential to design a cost‐effective and automated solution for precise water allocation in various agricultural and hydrological applications. Automated machine learning (AutoML), a substantially progressive machine learning (ML) paradigm, expedites the data science applications in these domains by automating and alleviating the tedious tasks of developing ML pipelines, particularly in non‐linear hydrological process modelling. This paper proposes an AutoML solution for daily EToprediction in a limited input parameter scenario, for the first time in EToestimation research. Two AutoML frameworks, AutoGluon‐Tabular (AGT) and H2O AutoML were implemented using daily meteorological data of a humid tropical climatic region of Kerala, India and evaluated its performance with the radiation‐based empirical methods and traditional ML methods. The developed AutoML models imitated the input parameter combinations of radiation‐based empirical models such as Priestley‐Taylor, Makkink, Turc and Ritchie, with the Penman‐Monteith EToserving as the target for modelling process. An improvement of mean absolute error from 0.119 to 0.078 mm/day, mean squared error from 0.035 to 0.017 mm/day, root mean squared error from 0.186 to 0.129 mm/day, root mean squared logarithmic error from 0.078 to 0.026 mm/day, and coefficient of determination (R2) from 0.971 to 0.985 observed in the AutoML model, AGT relative to the empirical model, Turc describes its enhanced performance capability. For demonstrating the superiority of AutoML to traditional ML techniques, another set of AutoML models were also developed based on California Irrigation Management Information System datasets and evaluated them with existing study results in literature. The overall result analysis demonstrated the superiority of AGT model in EToprediction in all the weather stations considered in this study. The results of the study confirm that AutoML techniques can be applied to any hydrological dataset and provide an effective automated solution for hydrological process modelling.

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Smart IoT Watering Platform Based on Orchestration: A Case Study

Gualpa,

Ayala,

Cáceres

et al. 2022

Lecture Notes in Networks and Systems

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Comparison of deep neural networks for reference evapotranspiration prediction using minimal meteorological data

Cited by 12 publications

References 36 publications

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

Smart IoT Watering Platform Based on Orchestration: A Case Study

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