k-Nearest Neighbor Regression in the Estimation of Tectona Grandis Trunk Volume in the State of Pará, Brazil

Souza, Deivison Venicio; Nievola, Júlio César; Santos, Joielan Xipaia dos; Wojciechowski, Jaime; Gonçalves, Alexandre Leão; Côrte, Ana Paula Dalla; Sanquetta, Carlos Roberto

doi:10.1080/10549811.2019.1607391

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Martinez et al, have used KNN to deal with seasonality in the time series forecasting problem by assigning specialized KNN for every season and made forecasting [14]. Souza et al, have used KNN regression model to forecast trunk volume in a city and their opinion is using KNN unless classical methods are far away from providing good results [15]. In another study, Ghiassi et al, have forecasted water demand of a metropolitan using KNN besides other machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors

Güven

Uygun

Şimşir

2021

Tekstil Ve Konfeksiyon

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Demand forecasting is a key factor for apparel retail stores to sustain their business, especially where there are variety of products and intermittent demand. In this study, two of the most popular machine learning methods, random forest (RF) and k-nearest neighbour (KNN), have been used to forecast retail apparel's intermittent demand. Numerous variables that may have an effect on the sales, have been taken into account one of which is defined as "special day" that might trigger intermittency in the demand. During the forecast application, four different datasets were used to provide reliability. 28 different variables were used to increase accuracy of the forecasting and experience of the behaviours of the algorithms. Root mean square error (RMSE) was used to evaluate performance of the methods and as a result of this study, RF showed better performance in all four datasets comparing to KNN.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors

Güven

Uygun

Şimşir

2021

Tekstil Ve Konfeksiyon

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Parameter-free surrounding neighborhood based regression methods

İnkaya

2022

Expert Systems with Applications

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Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

Zhang,

Xu,

She

et al. 2024

Plant Methods

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Background Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. Results The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01). Conclusions The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

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k-Nearest Neighbor Regression in the Estimation of Tectona Grandis Trunk Volume in the State of Pará, Brazil

Cited by 4 publications

References 19 publications

Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors

Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors

Parameter-free surrounding neighborhood based regression methods

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

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