Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information

Kim, Kwang-Hyung; Cho, Jaepil

doi:10.1007/s10584-015-1503-2

Cited by 20 publications

(11 citation statements)

References 25 publications

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“…Statistical downscaling was conducted using the method implemented by Cho (2013) and Kim and Cho (2016). Briefly, we selected daily scale scenario data from 11 GCMs (information on the GCMs refers to Table 1 in Kim and Cho, 2016). The scenario data of 11 GCMs were bias-corrected and spatially downscaled to the 7 ASOS stations using the non-parametric quantile mapping method.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change

Kim

Koh

2019

Plant Pathol J

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Section: Methodsmentioning

confidence: 99%

An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change

Kim

Koh

2019

Plant Pathol J

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“…Considering all these factors, we found that the EPIRICE model from our previous study closely met most of the abovementioned requirements ( Kim et al, 2015 ). Because of its broad genericity and simplicity but sound infection algorithms, EPIRICE has been adopted in many modeling-based studies worldwide ( Duku et al, 2016 ; Hensawang et al, 2017 ; Kim and Cho, 2016 ; Kim et al, 2015 ; Sittisak et al, 2017 ). In this respect, our objective was to develop an SCF-compatible disease epidemiological model by extracting and modifying the core infection algorithms of the EPIRICE model.…”

mentioning

confidence: 99%

Development of a Daily Epidemiological Model of Rice Blast Tailored for Seasonal Disease Early Warning in South Korea

Kim

Jung

2020

Plant Pathol J

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“…Kwan-Hyung Kim [4] evaluated EPIRICE model with historical disease incidence data and weather data from 2002-2010. This evaluation is carried out in South Korea.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

M.¹

2020

IJRITCC

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Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers 4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

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Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information

Cited by 20 publications

References 25 publications

An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change

An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change

Development of a Daily Epidemiological Model of Rice Blast Tailored for Seasonal Disease Early Warning in South Korea

A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

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