Using ensemble learning method and binary decision tree algorithm for drought intensity level classification

Oksana Kukartseva, Vadim Tynchenko, Vladislav Kukartsev, Tatyana Panfilova

Article ID: 6807
Vol 8, Issue 10, 2024

VIEWS - 791 (Abstract)

Abstract


This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.


Keywords


sustainable growth; agricultural development; land management; soil fertility; agricultural innovation

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References

  1. Aghelpour, P., Mohammadi, B., Biazar, S. M., et al. (2020). A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information, 9(12), 701. https://doi.org/10.3390/ijgi9120701
  2. Achite, M., Jehanzaib, M., Elshaboury, N., et al. (2022). Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria. Water, 14(3), 431. https://doi.org/10.3390/w14030431
  3. Aghelpour, P., Mohammadi, B., Biazar, S. M., et al. (2020). A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information, 9(12), 701. https://doi.org/10.3390/ijgi9120701
  4. Alkan, A. (2023). Drought Forecasting using Palmer Drought Severity Index with Wavelet Transform Technique and Machine Learning Methods. International Journal of Research Publication and Reviews, 04(01), 2177–2185. https://doi.org/10.55248/gengpi.2023.4158
  5. Almikaeel, W., Čubanová, L., & Šoltész, A. (2022). Hydrological Drought Forecasting Using Machine Learning—Gidra River Case Study. Water, 14(3), 387. https://doi.org/10.3390/w14030387
  6. Bashmur, K. A., Kolenchukov, O. A., Bukhtoyarov, V. V., et al. (2022). Biofuel Technologies and Petroleum Industry: Synergy of Sustainable Development for the Eastern Siberian Arctic. Sustainability, 14(20), 13083. https://doi.org/10.3390/su142013083
  7. Borodulin, A., Gladkov, A., Gantimurov, A., et al. (2024). Using machine learning algorithms to solve data classification problems using multi-attribute dataset. BIO Web of Conferences, 84, 02001. https://doi.org/10.1051/bioconf/20248402001
  8. Bosikov, I. I., Martyushev, N. V., Klyuev, R. V., et al. (2023). Modeling and Complex Analysis of the Topology Parameters of Ventilation Networks When Ensuring Fire Safety While Developing Coal and Gas Deposits. Fire, 6(3), 95. https://doi.org/10.3390/fire6030095
  9. C.M, A. M., Chowdary, V. M., Kesarwani, M., et al. (2022). Integrated drought monitoring and assessment using multi-sensor and multi-temporal earth observation datasets: a case study of two agriculture-dominated states of India. Environmental Monitoring and Assessment, 195(1). https://doi.org/10.1007/s10661-022-10550-6
  10. Chu, H. J. (2018). Drought Detection of Regional Nonparametric Standardized Groundwater Index. Water Resources Management, 32(9), 3119–3134. https://doi.org/10.1007/s11269-018-1979-4
  11. Degtyareva, K., Ageev, D. A., & Kukartsev, V. V. (2023). Finding patterns in employee attrition rates using self-organizing Kohonen maps and decision trees. 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). https://doi.org/10.1109/icses60034.2023.10465548
  12. Dehghani, M., Saghafian, B., Nasiri Saleh, F., et al. (2013). Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte‐Carlo simulation. International Journal of Climatology, 34(4), 1169–1180. https://doi.org/10.1002/joc.3754
  13. Deo, R. C., & Şahin, M. (2015). Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmospheric Research, 153, 512–525. https://doi.org/10.1016/j.atmosres.2014.10.016
  14. Deo, R. C., Tiwari, M. K., Adamowski, J. F., et al. (2016). Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic Environmental Research and Risk Assessment, 31(5), 1211–1240. https://doi.org/10.1007/s00477-016-1265-z
  15. Dikshit, A., Pradhan, B., & Huete, A. (2021). An improved SPEI drought forecasting approach using the long short-term memory neural network. Journal of Environmental Management, 283, 111979. https://doi.org/10.1016/j.jenvman.2021.111979
  16. Durand, M., Molotch, N. P., & Margulis, S. A. (2008). Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction. Remote Sensing of Environment, 112(3), 1212–1225. https://doi.org/10.1016/j.rse.2007.08.010
  17. Elbeltagi, A., Pande, C. B., Kumar, M., et al. (2023). Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environmental Science and Pollution Research, 30(15), 43183–43202. https://doi.org/10.1007/s11356-023-25221-3
  18. Fung, K. F., Huang, Y. F., & Koo, C. H. (2019). Coupling fuzzy–SVR and boosting–SVR models with wavelet decomposition for meteorological drought prediction. Environmental Earth Sciences, 78(24). https://doi.org/10.1007/s12665-019-8700-7
  19. Gohel, H. A., Upadhyay, H., Lagos, L., et al. (2020). Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nuclear Engineering and Technology, 52(7), 1436–1442. https://doi.org/10.1016/j.net.2019.12.029
  20. Khan, N., Sachindra, D. A., Shahid, S., et al. (2020). Prediction of droughts over Pakistan using machine learning algorithms. Advances in Water Resources, 139, 103562. https://doi.org/10.1016/j.advwatres.2020.103562
  21. Kolachian, R., & Saghafian, B. (2021). Hydrological drought class early warning using support vector machines and rough sets. Environmental Earth Sciences, 80(11). https://doi.org/10.1007/s12665-021-09536-3
  22. Kolenchukov, O. A., Bashmur, K. A., Bukhtoyarov, V. V., et al. (2022). Experimental Study of Oil Non-Condensable Gas Pyrolysis in a Stirred-Tank Reactor for Catalysis of Hydrogen and Hydrogen-Containing Mixtures Production. Energies, 15(22), 8346. https://doi.org/10.3390/en15228346
  23. Kozlova, A., Kukartsev, V., Melnikov, V., et al. (2023). Finding dependencies in the corporate environment using data mining. E3S Web of Conferences, 431, 05032. https://doi.org/10.1051/e3sconf/202343105032
  24. Liu, J., Jiang, W., Han, H., et al. (2023). Drought Level Prediction Based on Meteorological Data and Deep Learning. In: Proceedings of the 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). https://doi.org/10.1109/secon58729.2023.10287478
  25. Malozyomov, B. V., Martyushev, N. V., Kukartsev, V. A., et al. (2023). Study of Supercapacitors Built in the Start-Up System of the Main Diesel Locomotive. Energies, 16(9), 3909. https://doi.org/10.3390/en16093909
  26. Martyushev, N. V., Bublik, D. A., Kukartsev, V. V., et al. (2023). Provision of Rational Parameters for the Turning Mode of Small-Sized Parts Made of the 29 NK Alloy and Beryllium Bronze for Subsequent Thermal Pulse Deburring. Materials, 16(9), 3490. https://doi.org/10.3390/ma16093490
  27. Mokhtar, A., Jalali, M., He, H., et al. (2021). Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. IEEE Access, 9, 65503–65523. https://doi.org/10.1109/access.2021.3074305
  28. Mosavi, A., & Ardabili, S. (2023). Machine Learning for Drought Prediction; Review, Bibliometric Analysis, and Models Evaluation. In: Proceedings of the 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES). https://doi.org/10.1109/ines59282.2023.10297771
  29. Rahmati, O., Falah, F., Dayal, K. S., et al. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of The Total Environment, 699, 134230. https://doi.org/10.1016/j.scitotenv.2019.134230
  30. Raza, M. A., Almazah, M. M. A., Ali, Z., et al. (2022). Application of Extreme Learning Machine Algorithm for Drought Forecasting. Complexity, 2022, 1–28. https://doi.org/10.1155/2022/4998200
  31. Rhee, J., & Im, J. (2017). Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Agricultural and Forest Meteorology, 237–238, 105–122. https://doi.org/10.1016/j.agrformet.2017.02.011
  32. Richman, M. B., Leslie, L. M., & Segele, Z. T. (2016). Classifying Drought in Ethiopia Using Machine Learning. Procedia Computer Science, 95, 229–236. https://doi.org/10.1016/j.procs.2016.09.319
  33. Shah, H., Rane, V., Nainani, J., et al. (2017). Drought Prediction and Management using Big Data Analytics. International Journal of Computer Applications, 162(4), 27–30. https://doi.org/10.5120/ijca2017913276
  34. Shamshirband, S., Hashemi, S., Salimi, H., et al. (2020). Predicting Standardized Streamflow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 339–350. https://doi.org/10.1080/19942060.2020.1715844
  35. Sharma, P., Kar, B., Wang, J., et al. (2021). A machine learning approach to flood severity classification and alerting. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities. https://doi.org/10.1145/3486626.3493432
  36. Tufaner, F., & Özbeyaz, A. (2020). Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms. Environmental Monitoring and Assessment, 192(9). https://doi.org/10.1007/s10661-020-08539-0
  37. Tynchenko, Y., Kukartsev, V., Gladkov, A., et al. (2024). Assessment of technical water quality in mining based on machine learning methods. Sustainable Development of Mountain Territories, 16(1), 56–69. https://doi.org/10.21177/1998-4502-2024-16-1-56-69
  38. Vrindavanam, J., Babu, T., Gandiboina, H., et al. (2022). A Comparative Analysis of Machine Learning Algorithms for Agricultural Drought Forecasting. In: Proceedings of the 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). https://doi.org/10.1109/icict55121.2022.10064511
  39. Yelemessov, K., Isametova, M., Saydinbayeva, N., et al. (2023). Mathematical and computer modeling of gantry crane load-beam system oscillation. Sustainable Development of Mountain Territories, 15(2), 450–461. https://doi.org/10.21177/1998-4502-2023-15-2-450-461
  40. Zhao, Y., Zhang, J., Bai, Y., et al. (2022). Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors. Remote Sensing, 14(24), 6398. https://doi.org/10.3390/rs14246398


DOI: https://doi.org/10.24294/jipd.v8i10.6807

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