SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Pattimura
2024-10-01
|
| Series: | Barekeng |
| Subjects: | |
| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12959 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849237885103898624 |
|---|---|
| author | Helda Yunita Taihuttu Imas Sukaesih Sitanggang Lailan Syaufina |
| author_facet | Helda Yunita Taihuttu Imas Sukaesih Sitanggang Lailan Syaufina |
| author_sort | Helda Yunita Taihuttu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e8f4e46e892149abaa3a31446361e906 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-e8f4e46e892149abaa3a31446361e9062025-08-20T04:01:48ZengUniversitas PattimuraBarekeng1978-72272615-30172024-10-011842505251610.30598/barekengvol18iss4pp2505-251612959SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSORHelda Yunita Taihuttu0Imas Sukaesih Sitanggang1Lailan Syaufina2Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Silviculture, Faculty of Forestry, IPB University, IndonesiaSoil 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.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12959forest and land fireprediction modelrandom forest regressorrandomized search cvsoil moisture |
| spellingShingle | Helda Yunita Taihuttu Imas Sukaesih Sitanggang Lailan Syaufina SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR Barekeng forest and land fire prediction model random forest regressor randomized search cv soil moisture |
| title | SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR |
| title_full | SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR |
| title_fullStr | SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR |
| title_full_unstemmed | SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR |
| title_short | SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR |
| title_sort | soil moisture prediction model in peatland using random forest regressor |
| topic | forest and land fire prediction model random forest regressor randomized search cv soil moisture |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12959 |
| work_keys_str_mv | AT heldayunitataihuttu soilmoisturepredictionmodelinpeatlandusingrandomforestregressor AT imassukaesihsitanggang soilmoisturepredictionmodelinpeatlandusingrandomforestregressor AT lailansyaufina soilmoisturepredictionmodelinpeatlandusingrandomforestregressor |