SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR
Abstract
Soil moisture is one of the factors that has recently become the focus of research because it is strongly correlated with forest and land fires, where low soil moisture will increase drought and the incidence of forest and land fires. For this reason, this study aims to create a prediction model for soil moisture as an early prevention of fires in peatlands using the Random Forest Regressor (RFR) algorithm. RFR is used because of its ability to predict values and its resistance to overfitting and outliers. A dataset covering soil moisture, precipitation, temperature, maturity, and peat thickness was collected from August 2019 to December 2023. The data includes soil moisture, precipitation, temperature, maturity, and peat thickness. The data were divided into 80% for modeling and 20% for testing. Model performance was optimized through random search CV, resulting in significant prediction accuracy R-squared: 0.914, MAE: 0.0081, MSE: 0.0007, RMSE: 0 .0271, and MAPE: 0.969. These findings demonstrate the effectiveness of RFR in soil moisture prediction and pave the way for more appropriate and timelier implementation of fire mitigation strategies.
Downloads
References
KLHK, “Identifikasi Areal Bekas Kebakaran Hutan Dan Lahan,” Jakarta, 2019. doi: 10.24895/sng.2017.2-0.420.
D. A. P. S. A.F. Falatehan, “Characteristics of Peat Biomass as an Alternative Energy and Its Impact on the Environment,” Solid State Technol., vol. 63, no. 5, 2020.
A. Sirin and J. Laine, Chapter 7 : Peatlands and greenhouse gases. 2011.
C. Buschmann et al., “Land Use Policy Perspectives on agriculturally used drained peat soils : Comparison of the socioeconomic and ecological business environments of six European regions,” Land use policy, vol. 90, no. January 2019, p. 104181, 2020, doi: 10.1016/j.landusepol.2019.104181.
L. U. Li et al., “Estimation of Ground Water Level ( GWL ) for Tropical Peatland Forest Using Machine Learning,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, no. October, 2022, doi: 10.1109/ACCESS.2022.3225906.
L. Syaufina, I. S. Sitanggang, and L. M. Erman, “Challenges in Satellite-based Research on Forest and Land Fires in Indonesia: Frequent Item Set Approach,” Procedia Environ. Sci., vol. 33, pp. 324–331, 2016, doi: 10.1016/j.proenv.2016.03.083.
Fajri, “Model Prediksi Temporal Tinggi Muka Air Sebagai Peringatan Dini Karhutla pada Lahan Gambut Menggunakan LSTM,” IPB University, 2022.
D. Chaparro, M. Vall-Llossera, M. Piles, A. Camps, C. Rudiger, and R. Riera-Tatche, “Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 6, pp. 2818–2829, 2016, doi: 10.1109/JSTARS.2016.2571838.
D. Jensen, J. T. Reager, B. Zajic, N. Rousseau, M. Rodell, and E. Hinkley, “The sensitivity of US wildfire occurrence to pre-season soil moisture conditions across ecosystems,” Environ. Res. Lett., vol. 13, no. 1, 2018, doi: 10.1088/1748-9326/aa9853.
J. T. Ambadan, M. Oja, Z. Gedalof, and A. A. Berg, “Satellite-Observed Soil Moisture As an Indicator of Wildfire Risk,” Remote Sens., vol. 12, no. 10, pp. 8–12, 2020, doi: 10.3390/rs12101543.
N. Sazib, J. D. Bolten, and I. E. Mladenova, “Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts over Australia and California,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, no. June, pp. 779–787, 2022, doi: 10.1109/JSTARS.2021.3136756.
L. Breiman, “Random Forests,” Manuf. Netherlands, vol. 45, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
V. M. Herrera, T. M. Khoshgoftaar, F. Villanustre, and B. Furht, Random forest implementation and optimization for Big Data analytics on LexisNexis’s high performance computing cluster platform, vol. 6, no. 1. Springer International Publishing, 2019. doi: 10.1186/s40537-019-0232-1.
L. Zhang et al., “In situ observation-constrained global surface soil moisture using random forest model,” Remote Sens., vol. 13, no. 23, pp. 1–25, 2021, doi: 10.3390/rs13234893.
C. Sanjeev, K. Dash, A. Kumar, S. Dehuri, and A. Ghosh, “An outliers detection and elimination framework in classification task of data mining,” Decis. Anal. J., vol. 6, no. January, p. 100164, 2023, doi: 10.1016/j.dajour.2023.100164.
A. Palanivinayagam and R. Damaševiˇ, “Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods,” vol. 14, no. 92, pp. 1–15, 2023.
H. Kang, “The prevention and handling of the missing data,” Korean Soc. Anesthesiol., vol. 64, no. 5, pp. 402–406, 2013.
A.-I. Udil, A. Ionescu, and A. Katsifodimos, “Encoding Methods for Categorical Data: A Comparative Analysis for Linear Models, Decision Trees, and Support Vector Machines.” 2023. [Online]. Available: http://repository.tudelft.nl/.
E. Luís and P. Da Silva, “Combining Combining machine learning and deep learning approaches to detect cervical cancer in cytology images.” 2021.
J. K. Afriyie et al., “A Supervised machine learning algorithm for detecting and predicting fraud in credit card transaction,” Decis. Anal. J., vol. 100, no. 163, 2023.
G. Louppe, Understanding Random Forests: From Theory to Practice, no. October 2014. 2014. doi: 10.13140/2.1.1570.5928.
E. Izquierdo-Verdiguier and R. Zurita-Milla, “An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing,” Int. J. Appl. Earth Obs. Geoinf., vol. 88, no. June 2019, 2020, doi: 10.1016/j.jag.2020.102051.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014, doi: 10.5194/gmd-7-1247-2014.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
Copyright (c) 2024 Helda Yunita Taihuttu, Imas Sukaesih Sitanggang, Lailan Syaufina
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
