Image Classification using Machine Learning Algorithms to Detect Cloud Types

Nova  Agustina; Candra Nur  Ihsan; Kelik  Sussolaikah

Nova Agustina Teknik Informatika, Sekolah Tinggi Teknologi Bandung
Candra Nur Ihsan Kecerdasan Artifisial dan Keamanan Siber, Badan Riset dan Inovasi Nasional
Kelik Sussolaikah Teknik Informatika, Universitas PGRI Madiun https://orcid.org/0000-0002-7401-622X

Keywords: image classification, cloud types, comparison, machine learning

Abstract

Study of atmospheric are currently growing rapidly to analyze the negative effects of climate change, weather and air quality. Unstable atmospheric conditions have a negative impact, as extreme weather. The combination of technology and analysis of atmospheric conditions is currently developing rapidly. While atmospheric research using machine learning technology and algorithms is advancing swiftly, challenges persist in identifying the optimal machine learning model for precise cloud type classification. The application of Machine Learning algorithms in atmospheric research has been widely carried out to predict wind direction and cloud imagery to detect weather using satellite data. Detecting cloud type is important for predicting the upcoming weather. However, to detect the type of cloud, it is necessary to choose the algorithm with the best performance. This study applies the Convolutional Neural Network (CNN) with EfficienNetB3 method, Support Vector Classifier (SVC), XGBoost Classifier (XGB), Extra Tree Classifier (ETC), Random Forest (RF), and Decision Tree (DT) algorithms in classifying cloud images to detect clouds type. The method used in this research involves an experimental approach in the hope of gaining a deeper understanding of the factors that influence the performance of machine learning models in classifying cloud types. The dataset used in this research is 1500 cloud data (1200 training data, 300 testing data). Researchers conducted a comparison of algorithms to find out the best algorithm performance in classifying cloud type images. The results showed that doing the CNN algorithm showed better performance with an average accuracy got of 81.03% compared to the SVC algorithm (34.44%), XGB (33.79%), ETC (39.25%), RF (36.18), and DT (29.35%). Our contribution to this research is that we compare machine learning algorithms to detect cloud types along with the impact and characteristics of cloud types from the prediction results.

Downloads

Download data is not yet available.

Author Biography

Kelik Sussolaikah, Teknik Informatika, Universitas PGRI Madiun

References

B. Clarke, F. Otto, R. Stuart-Smith, and L. Harrington, “Extreme weather impacts of climate change: an attribution perspective,” Environmental Research: Climate, vol. 1, no. 1, p. 012001, 2022, doi: 10.1088/2752-5295/ac6e7d.

Y. Lu, Y. Li, Q. Xia, Q. Yang, and C. Wang, “Interdecadal Change of Ural Blocking Highs and Its Atmospheric Cause in Winter during 1979–2018,” Atmosphere, vol. 13, no. 9, pp. 1–15, 2022, doi: 10.3390/atmos13091530.

F. Song, G. J. Zhang, V. Ramanathan, and L. R. Leung, “Trends in surface equivalent potential temperature: A more comprehensive metric for global warming and weather extremes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 6. 2022. doi: 10.1073/pnas.2117832119.

K. Ullah and F. Ikram, “Dynamics of Unusual Extreme Rainfall Events Over Different Altitudes Using Richardson Number,” Earth and Space Science, vol. 9, no. 8, pp. 1–9, Aug. 2022, doi: 10.1029/2022EA002283.

S. Soldatenko, “Estimated impacts of climate change on eddy meridional moisture transport in the atmosphere,” Applied Sciences (Switzerland), vol. 9, no. 23, pp. 1–24, 2019, doi: 10.3390/app9234992.

E. McDonald-Buller, G. McGaughey, J. Grant, T. Shah, Y. Kimura, and G. Yarwood, “Emissions and air quality implications of upstream and midstream oil and gas operations in Mexico,” Atmosphere, vol. 12, no. 12, 2021, doi: 10.3390/atmos12121696.

S. Schemm, D. Grund, R. Knutti, H. Wernli, M. Ackermann, and G. Evensen, “Learning from weather and climate science to prepare for a future pandemic,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 4. 2023. doi: 10.1073/pnas.2209091120.

BMKG, “Peraturan Badan Meteorologi, Klimatologi, Dan Geofisika Republik Indonesia Nomor 5 Tahun 2O2O,” 2020.

A. Sri and R. Wijatmiko, “Pemanfaatan Satellite Animation and Interactive Diagnosis untuk Analisis Kondisi Atmosfer saat Banjir di Kalukku menggunakan Metode Numerical Weather Prediction,” vol. 12, no. 2, pp. 65–75, 2022.

K. Tran-Trung, H. Duong Thi Hong, and V. Truong Hoang, “Weather Forecast Based on Color Cloud Image Recognition under the Combination of Local Image Descriptor and Histogram Selection,” Electronics (Switzerland), vol. 11, no. 21, pp. 0–14, 2022, doi: 10.3390/electronics11213460.

Y. Jiang et al., “A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites,” Remote Sensing, vol. 14, no. 10, 2022, doi: 10.3390/rs14102314.

V. Afzali Gorooh et al., “Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS,” Remote Sensing, vol. 12, no. 2, p. 316, Jan. 2020, doi: 10.3390/rs12020316.

S. Mahajan and B. Fataniya, “Cloud detection methodologies: variants and development—a review,” Complex and Intelligent Systems, vol. 6, no. 2, pp. 251–261, 2020, doi: 10.1007/s40747-019-00128-0.

P. Kuma, F. A.-M. Bender, A. Schuddeboom, A. J. McDonald, and Ø. Seland, “Machine learning of cloud types in satellite observations and climate models,” Atmospheric Chemistry and Physics, vol. 23, no. 1, pp. 523–549, 2023, doi: 10.5194/acp-23-523-2023.

S. M. Ayazi and M. Saadat Seresht, “Comparison of traditional and machine learning base methods for ground point cloud labeling,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 42, no. 4/W18, pp. 141–145, 2019, doi: 10.5194/isprs-archives-XLII-4-W18-141-2019.

J. Zhang, P. Liu, F. Zhang, and Q. Song, “CloudNet: Ground-Based Cloud Classification With Deep Convolutional Neural Network,” Geophysical Research Letters, vol. 45, no. 16, pp. 8665–8672, 2018, doi: 10.1029/2018GL077787.

M. Zhao, C. H. Chang, W. Xie, Z. Xie, and J. Hu, “Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM,” IEEE Access, vol. 8, pp. 44111–44124, 2020, doi: 10.1109/ACCESS.2020.2978090.

C. N. Ihsan, “Klasifikasi Data Radar Menggunakan Algoritma Convolutional Neural Network (CNN),” DoubleClick: Journal of Computer and Information Technology, vol. 4, no. 2, p. 115, 2021, doi: 10.25273/doubleclick.v4i2.8188.

X. Lai, Y. Yuan, Y. Li, and M. Wang, “Full-waveform LiDAR point clouds classification based on wavelet support vector machine and ensemble learning,” Sensors (Switzerland), vol. 19, no. 14, 2019, doi: 10.3390/s19143191.

X. Y. Liew, N. Hameed, and J. Clos, “An investigation of XGBoost-based algorithm for breast cancer classification,” Machine Learning with Applications, vol. 6, no. September, p. 100154, 2021, doi: 10.1016/j.mlwa.2021.100154.

T. E. Mathew, “an Optimized Extremely Randomized Tree Model for Breast Cancer Classification,” Journal of Theoretical and Applied Information Technology, vol. 100, no. 16, pp. 5234–5246, 2022.

D. Jollyta, G. Gusrianty, and D. Sukrianto, “Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 5, no. 2, pp. 134–139, 2019, doi: 10.23917/khif.v5i2.8263.

J. Feng, D. Wang, and Z. Gu, “Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification,” Remote Sensing, vol. 14, no. 16, 2022, doi: 10.3390/rs14163943.

BIKRAM SAHA, “Howard-Cloud-X.” 2022.

R. S. Budi, R. Patmasari, and S. Saidah, “Klasifikasi Cuaca Menggunakan Metode Convolutional Neural Network ( Cnn ),” e-Proceeding of Engineering, vol. 8, no. 5, pp. 5047–5052, 2021.

S. M. K. II and S. Noor, “Buletin Meterologi,” vol. X, no. 10, Banjarbaru: Badan Meteorologi, Klimatologi, dan Geofisika, 2022.

Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” Proceedings of the IEEE International Conference on Computer Vision, pp. 9992–10002, 2021, doi: 10.1109/ICCV48922.2021.00986.

H. Pramoedyo, D. Ariyanto, and N. N. Aini, “Comparison of Random Forest and Naïve Bayes Methods for Classifying and Forecasting Soil Texture in the Area Around Das Kalikonto, East Java,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 4, pp. 1411–1422, 2022, doi: 10.30598/barekengvol16iss4pp1411-1422.

X. Wan and J. Du, “Cloud classification for ground-based sky image using random forest,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 43, no. B3, pp. 835–842, 2020, doi: 10.5194/isprs-archives-XLIII-B3-2020-835-2020.

Z. Yu, S. Ma, D. Han, G. Li, D. Gao, and W. Yan, “A cloud classification method based on random forest for FY-4A,” International Journal of Remote Sensing, vol. 42, no. 9, pp. 3353–3379, May 2021, doi: 10.1080/01431161.2020.1871098.

suwarno and R. Kusnadi, “Analisis Perbandingan SVM, XGBoost dan Neural Network pada Klasifikasi,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 5, pp. 896–903, Oct. 2021, doi: 10.29207/resti.v5i5.3506.