Soil Infiltration Rate Prediction using Machine Learning Regression Model: A Case Study on Sepinggan River Basin, Balikpapan, Indonesia

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. Keywords: infiltration rate, initial water contents, grain size