Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

Yamaç, Sevim Seda; Todorović, Mladen

doi:10.1016/j.agwat.2019.105875

Cited by 129 publications

(73 citation statements)

References 37 publications

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“…Suitable data sets or parameters vary according to different regions as well as climate patterns. In order to aid future researchers during the process of data acquisition, significant parameters (though not exhaustive) to estimate ET for different climate patterns are summarized in Table 1 [27][28][29][30][31][32][33][34][35][36][37]. From Table 1, it can be seen that temperature and radiation data are indispensable for ET estimation.…”

Section: Data Typesmentioning

confidence: 99%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

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Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

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Section: Data Typesmentioning

confidence: 99%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

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“…On the subject of potato cultivation in farmland, various topics are highlighted and well-documented. Methods using machine learning algorithms are successfully applied to provide a solution on these topics such as predicting leaf water potential [26], modeling root development [27], tuber growth [28], daily evapotranspiration prediction [29,30,31], etc. The issue of evapotranspiration prediction is well documented, however concerning the SWP, to our knowledge, a methodology for weekly prediction of this indicator using a connected sensor-powered ML approach has not been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…• model-based, in this case a reference model (e.g. Penman-Monteith [31,33] or AquaCrop [14,34,27],…) are used, generally an optimization is performed to improve the quality of predictions.…”

Section: Introductionmentioning

confidence: 99%

Short term soil moisture forecasts for potato crop farming: A machine learning approach

Dubois

Teytaud

Vérel

2021

Computers and Electronics in Agriculture

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Agricultural decision-making is crucial for future yields. In the context of smart farming, grower combine information from sensors located close to their crops with agronomic models to help them to better understand their crops. Irrigation management is therefore based on extrapolation of data and/or agronomic model responses. This problem can be seen as a learning task for which machine learning techniques have proven their relevance in many and diverse applications. In this paper we place ourselves in the context of potato farming, a crop for which irrigation plays a crucial role. We model the problem of soil water potential prediction as a learning problem solved by supervised leaning algorithms. The problem appears to be difficult since there are several potential inputs, and several outputs to predict. Experiments are conducted on several scenarios with data acquired during 3 years. We demonstrate the possibility of applying feature selection method to automatically design models with features relevant to the problem at hand while having good performances. We have also demonstrated the relevance of the machine learning for this kind of problem, since the methods are able to correctly predict the next water potential values.

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“…ANFIS), regression and classification tree models (e.g. M5 tree & CHAID) and machine learning approaches (SVM & SVR) for simulating and modelling ET0 (Kişi and Öztürk, 2007;Shiri et al, 2014;Shiri et al 2015;Gocić et al, 2015;Yin et al 2017;Mehdizadeh et al 2017;Dou and Yang 2018;Zounemat-Kermani et al, 2019;Chia et al, 2020;Chen et al, 2020;Yamaç and Todorovic, 2020;Adnan et al, 2021). In the following paragraphs some of the most recent pertinent studies about modelling ET using AI-based models are presented.…”

Section: Introductionmentioning

confidence: 99%

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Kişi

Keshtegar

Zounemat‐Kermani

et al. 2021

Preprint

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In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET0), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The radial-based kernel function was used to control the input variables in modelling process of M5 model tree. The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM) and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (NSE > 0.997) followed by the MLPNN (NSE > 0.990), and M5Tree (NSE > 0.945) in modelling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modelling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.

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Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

Cited by 129 publications

References 37 publications

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Short term soil moisture forecasts for potato crop farming: A machine learning approach

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

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