Flow Prediction Versus Flow Simulation Using Machine Learning Algorithms

Čistý, Milan; Soldanova, Veronika

doi:10.1007/978-3-319-96133-0_28

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…The extreme gradient boosting (XGBoost) method is y for classification. XGBoost controls the overfitting by using the regularized model formalization, which resulted in better performance compared to the previous boosted algorithms [ 85 ]. XGBoost consists of a few hyperparameters such as nrounds (the number of trees), eta , learning rate and depth (the depth of the tree) [ 86 ] that can be optimized to improve the performance.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Sarkar

Zhou

Scaboo

et al. 2023

Plants

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Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively.

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Section: Methodsmentioning

confidence: 99%

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Sarkar

Zhou

Scaboo

et al. 2023

Plants

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“…XGBoost is an ensemble of decision trees; it consists of a sequential build-off from various decision trees where each tree works to enhance the performance of the prior tree. It is considered superior to previous boosting algorithms because it uses a more regularized model formulation, which allows for better control of overfitting and ultimately improved performance (Cisty and Soldanova, 2018). Huber et al (2022) have recently shown that a feature-based representation derived from remote sensing can outperform even the most recent deep learning-based yield predictors.…”

Section: Modelling Methodology 261 Yield Estimationmentioning

confidence: 99%

Out-of-Year Corn Yield Prediction at Field-Scale Using Sentinel-2 Satellite Imagery and Machine Learning Methods

Desloires¹,

Ienco²,

Botrel³

2023

Preprint

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“…Net solar radiation, heat flux, moisture content, wind-speed, mean moisture, mean temperature [6] Multiple linear regression, random forest, extreme gradient boosting, and deep learning neural network Meteorological data (precipitation, temperatures and potential evapotranspiration) [7] Partial least square regression, and adaptive neuro fuzzy inference systems Soil moisture, soil temperature, rain fall, wind speed, crop evapotranspiration, radiation, dew point [8] Support vector regression, and SVR + K-means Moisture, temperature, UV, weather temperature, humidity, and precipitation computing, the highly voted predictions are selected as a final output. Random forest empowers an enormous set of weak classifiers to build a robust classifier [15].…”

Section: Methodsmentioning

confidence: 99%

“…Among these methods, the application of M5P regression trees, bagging, random forests, and regression support vectors to data from an experimental site in Central Florida according to [5]. Likewise, a comparison of two types of streamflow modeling was performed [6] using machine learning algorithms. The first one is based only on climatic data (precipitation, temperatures, and potential evapotranspiration), the second one integrates also the previous flows in the data entrees.…”

Section: Related Workmentioning

confidence: 99%

Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

2021

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Up-to-date, given the expanding increase of the population and the development of human daily lifestyles, the expenditure of freshwater resources increments progressively. It appears that there is a need to optimize at least the consumption of fresh water in agriculture. For this reason, novel various irrigation technologies have been deployed in this context like drip irrigation, flood irrigation, and decision support systems to come up with the constraints of climate changes that decrease the water availability but it is still limited. Therefore, the majority of researchers are working until today on automating the irrigation systems. These smart systems rely mainly on the advances of information technologies like the internet of things, big data, and machine learning for aligning irrigations with climatic changes. Besides, integrating the predictive process helps in anticipating and adapting to the climatic constraints in agriculture, using meticulous soil and environment dependencies analysis based on features' prediction. In this paper, we enriched our proposed flexible online learning (OL) framework designed for promoting irrigation decisions based on soil characteristics analysis and prediction. We shed the light on a comparative study of four predictive methods, in particular, the auto-regressive moving average, the eXtreme Gradient Boosting, the random forest, and the deep artificial neural networks implemented inside the Hadoop/Spark environment to predict the humidity of the soil, relying on soil temperature and time in several depths. In the end, we discussed the precision of these models in various conditions.

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Flow Prediction Versus Flow Simulation Using Machine Learning Algorithms

Cited by 13 publications

References 13 publications

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Out-of-Year Corn Yield Prediction at Field-Scale Using Sentinel-2 Satellite Imagery and Machine Learning Methods

Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

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