Overview: Random Forest Algorithm for PM2.5 Estimation Based on Remote Sensing

Tinjauan: Algoritma Random Forest untuk Estimasi PM2.5 Berbasis Pengindraan Jauh

Abstract

Studi ini merangkum penelitian tentang estimasi PM2.5 menggunakan algoritme pembelajaran mesin random forest (RF), penginderaan jauh, dan keduanya. Tujuan dari tinjauan ini adalah menyajikan studi yang komprehensif untuk memfasilitasi dan menentukan batasan, luas dan kedalaman pengetahuan yang dieksplorasi untuk memperkirakan konsentrasi PM2.5 di masa depan menggunakan RF dan pengindraan jauh. PM2.5 merupakan parameter lingkungan atmosfer yang penting, terutama karena dampaknya terhadap kesehatan manusia dan lingkungan. Terlepas dari skala spasial-temporal, perkiraan PM2.5 yang akurat penting untuk memahami dan menanggapi berbagai efek buruk dari polusi udara. Oleh karena itu, metode penginderaan jauh dan pembelajaran mesin dikembangkan untuk mendapatkan estimasi PM2.5 resolusi tinggi dan mengurangi kesalahan penilaian yang disebabkan oleh dislokasi spasial. Sejak penggunaan pertama jaringan saraf (NN) untuk mempelajari hubungan kompleks AOD-PM2.5, lebih dari 40 artikel terkait ML telah diterbitkan dalam dekade terakhir, dan lebih dari 90% di antaranya telah diterbitkan dalam lima tahun dan 75% dalam tiga tahun terakhir. Metode validasi yang mempertimbangkan pola spasial dalam validasi model ML mengungkapkan bahwa RF dan BPNN adalah yang paling populer digunakan.

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Published
2022-12-25
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