Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India

Saha, Sunil; Saha, Mantosh; Mukherjee, Kaustuv; Arabameri, Alireza; Ngo, Phuong Thao Thi; Paul, Goutam

doi:10.1016/j.scitotenv.2020.139197

Cited by 93 publications

(37 citation statements)

References 83 publications

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“…But now, scholars prefer machine learning algorithms of RF model for assessing the importance degree mainly in the fields of climate, hydrology, environment, etc. (Rahman and Islam 2019 ; Salam and Islam 2020 ; Saha et al 2020 ). In this study, a RF model was used followed by Rahman an Islam et al ( 2019 ), Salam and Islam ( 2020 ), Saha et al ( 2020 ), Islam et al ( 2020b ) to explore the importance degree of climatic variables influencing COVID-19 mortality cases across eight major divisional cities of Bangladesh.…”

Section: Methodsmentioning

confidence: 99%

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

Islam

Hasanuzzaman

Shammi

et al. 2020

Environ Sci Pollut Res

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Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, − 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by − 14.2% (95% CI 9.73%, − 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh.

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Section: Methodsmentioning

confidence: 99%

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

Islam

Hasanuzzaman

Shammi

et al. 2020

Environ Sci Pollut Res

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“…The identification of areas with a recent loss of forest cover allows to predict the future trend of forest cover and can give an overview of how deforestation will occur (Saha et al., 2020). The loss of tree cover was gathered from Global Forest Watch (2020), which includes the forest loss for the years 2000 to 2019.…”

Section: Methodsmentioning

confidence: 99%

Assessing the conservation of Miombo timber species through an integrated index of anthropogenic and climatic threats

Catarino

Romeiras

Pereira

et al. 2021

Ecology and Evolution

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“…Random Forest: is an ensemble learning method used for classification, regression, and other tasks. It is a machine learning ensemble model that creates several trees to execute a classification [31]. By using the ensemble method, the classification tree becomes more precise than an individual member.…”

Section: B Ensemble Classifiersmentioning

confidence: 99%

ElStream: An Ensemble Learning Approach for Concept Drift Detection in Dynamic Social Big Data Stream Learning

et al. 2021

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With the rapid increase in communication technologies and smart devices, an enormous surge in data traffic has been observed. A huge amount of data gets generated every second by different applications, users, and devices. This rapid generation of data has created the need for solutions to analyze the change in data over time in unforeseen ways despite resource constraints.These unforeseeable changes in the underlying distribution of streaming data over time are identified as concept drifts. This paper presents a novel approach named ElStream that detects concept drift using ensemble and conventional machine learning techniques using both real and artificial data. ElStream utilizes the majority voting technique making only optimum classifier to vote for decision. Experiments were conducted to evaluate the performance of the proposed approach. According to experimental analysis, the ensemble learning approach provides a consistent performance for both artificial and real-world data sets. Experiments prove that the ElStream provides better accuracy of 12.49%, 11.98%, 10.06%, 1.2%, and 0.33% for PokerHand, LED, Random RBF, Electricity, and SEA dataset respectively, which is better as compared to previous state-ofthe-art studies and conventional machine learning algorithms.

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Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India

Cited by 93 publications

References 83 publications

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

Assessing the conservation of Miombo timber species through an integrated index of anthropogenic and climatic threats

ElStream: An Ensemble Learning Approach for Concept Drift Detection in Dynamic Social Big Data Stream Learning

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