Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland

Chen, Han; Huang‬‬‬‬, Jinhui Jeanne; McBean, Edward

doi:10.1016/j.agwat.2019.105923

Cited by 69 publications

(17 citation statements)

References 55 publications

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“…One way to deal with the data-driven approach is the Machine Learning (ML). In recent years, Machine Learning methods have been successfully applied to many real world applications, and in particular [21,22,23] demonstrate the relevance of modeling complex biological phenomena such as evapotranspiration by transformation into a supervised learning problem solved by algorithms as Neural Network (NN), Random Forest (RF) or Support Vector Machine (SVM). [24] pointed out that ML approaches, in most cases, outperformed parametric approaches for the prediction of biomass and soil moisture from satellite data.…”

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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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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“…In any case, there are many models applied to estimate the ET o around the world. Examples of such are the constant heat method by including heat pulse [27,28] and the Shuttleworth-Wallace S-W method to estimate the transpiration from plants [29][30][31]. It is worth mentioning that the number of empirical equations for modeling the evaporation has exceeded 100 due to the importance of ET o measurements and the variety of meteorological data around the world.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

et al. 2021

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Reference evapotranspiration ET o is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly mean ET o t over Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression MLR , random forest RF , and extreme learning machine ELM . Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such as MLR and RF . Moreover, among the 10 statistical measures, the uncertainty at 95% U 95 indicator showed an excellent ability to select the best and most efficient forecast model. The superiority of ELM in the prediction of mean monthly ET o over MLR and RF approaches is illustrated in the reduction of the U 95 parameter to 49.02% and 34.07% for RF and MLR models, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate the ET o using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.

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“…Among various machine learning algorithms, tree-based algorithms such as Random Forests, Regression Trees, and M5 Model Tree have recently been gained significant attention due to their simplicity, robustness, and capability to provide accurate predictions of ET0 (Chen et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Roy

Saha

Kamruzzaman

et al. 2021

Water Resour Manage

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Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland

Cited by 69 publications

References 55 publications

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

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

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

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