Establishing Diagnosis Systems for Impaired Stream Ecosystem using Stream/River Ecosystem Survey and Health Assessment.

Leejongwon,; Lee, Sang‐Woo; Hwang, Soon‐Jin; Jang, Min‐Ho; Won, Doo Hee; An, Kyung Jin; Park, Hye-Jin; Lee, Junga

doi:10.11614/ksl.2020.53.1.001

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…A model using a random forest algorithm (RF), which is a machine learning method, was developed to predict the daily mosquito occurrence under two different landscape conditions defined in the HCA. RF is a useful method for the classification and prediction of ecological data with a high level of accuracy (Cutler et al 2007 ; Lee et al 2020 ). In the previous study (Kwon et al 2015 ) conducted at the same study sites, the RF model showed a better performance in predicting the mosquito occurrence than other machine learning methods such as the support vector machine and the classification and regression tree.…”

Section: Methodsmentioning

confidence: 99%

Section: Prediction Model For Mosquito Occurrence and Evaluationmentioning

confidence: 99%

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Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea

Lee

Park

2022

Environ Sci Pollut Res

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Mosquitoes are the underlying cause of various public health and economic problems. In this study, patterns of mosquito occurrence were analyzed based on landscape and meteorological factors in the metropolitan city of Seoul. We evaluated the influence of environmental factors on mosquito occurrence through the interpretation of prediction models with a machine learning algorithm. Through hierarchical cluster analysis, the study areas were classified into waterside and non-waterside areas, according to the landscape patterns. The mosquito occurrence was higher in the waterside area, and mosquito abundance was negatively affected by rainfall at the waterside. The mosquito occurrence was predicted in each cluster area based on the landscape and cumulative meteorological variables using a random forest algorithm. Both models exhibited good performance (both accuracy and AUROC > 0.8) in predicting the level of mosquito occurrence. The embedded relationship between the mosquito occurrence and the environmental factors in the models was explained using the Shapley additive explanation method. According to the variable importance and the partial dependence plots for each model, the waterside area was more influenced by the meteorological and land cover variables than the non-waterside area. Therefore, mosquito control strategies should consider the effects of landscape and meteorological conditions, including the temperature, rainfall, and the landscape heterogeneity. The present findings can contribute to the development of mosquito forecasting systems in metropolitan cities for the promotion of public health.

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Section: Methodsmentioning

confidence: 99%

Section: Prediction Model For Mosquito Occurrence and Evaluationmentioning

confidence: 99%