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- Title
Soil Infiltration Rate Prediction using Machine Learning Regression Model: A Case Study on Sepinggan River Basin, Balikpapan, Indonesia.
- Authors
SULISTYO, TOTOK; FAUZI, ROHMAT
- Abstract
The infiltration rate of soil data is important in a wide range of planning, such as city planning, drainage design, landuse planning, flood prediction, flood disaster mitigation, etc. Collecting data of infiltration through in-site direct measurements is time consuming and costly. Indeed, inferring the infiltration rate using available parameters and the fittest model is needed. The model can shortcut the field measurement to get a predicted accurate infiltration rate that is worthy to support vital planning. This research aims to develop a model of infiltration rate based on initial water contents and grain size of soils. The results are three outstanding models based on the Multiple R Squared, Root Mean Square Error (RMSE), and Mean Average Error (MAE). The implication of the fittest model is reducing the cost and time to get the predicted infiltration rate. The field measurements can be skipped by sampling undisturbed soils and laboratory tests.
- Subjects
SOIL infiltration; MACHINE learning; WATERSHEDS; STANDARD deviations; SOIL sampling
- Publication
Indonesian Journal on Geoscience, 2023, Vol 10, Issue 3, p335
- ISSN
2355-9314
- Publication type
Article
- DOI
10.17014/ijog.10.3.335-347