Prediction of Experimental Rainfall-Eroded Soil Area Based on S-Shaped Growth Curve Model Framework

Abstract

Rainfall-induced soil erosion of a mountain area plays a significant role in supplying sediment and shaping the landscape. The related area of soil erosion, as an index of the changed landscape, is easier to calculate visually using some popular imaging tools. By image analysis, our work shows that the changing of the soil erosion area admits the structure of an S-growth curve. Therefore, we propose to establish an S-curve model, based on incremental learning, to predict the soil erosion area. In the process of incremental learning, we dynamically update the accumulative rainfall and rainfall intensity to train the parameters of our S-curve model. In order to verify our prediction model, the index of area is utilized to express the output of eroded soil in a series of experiments. The results show that the proposed S-growth curve model can be used to estimate the growth of the soil OPEN ACCESSAppl. Sci. 2015, 5 158 erosion area (average relative error 3%-9.7%) according to variable soil material and rainfall intensity. The original S-growth curve model can calculate the erosion areas of just one soil material and one rainfall condition whose average relative error is 7.5%-12.2%; compared to the simple time series analysis-moving average method (average relative error 5.7%-12.1%), our proposed S-growth curve model can reveal the physical mechanism and evolution of the research object.

Comparison of Moving Average-Based, S Growth-Based, and AUF Rainfall-Induced Soil Erosion Area Models

“…Experimental schemes are in Table.1. (More details see [5]) The results of experiments are as shown in Fig. 1(b).…”

“…Especially, Nie et al [5] created an S-growth model for eroded soil area prediction based on a physical experiment. Following that, Fan et al [6] used a moving average model for rainfall induced soil eroded prediction.…”

“…Following that, Fan et al [6] used a moving average model for rainfall induced soil eroded prediction. In our study, we develop an AUF model for rainfall-induced soil erosion prediction (the experimental data is from [5]). The comparison and discussion of three models are given.…”

Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model

“…An incremental learning algorithm is introduced which considers the parameters of the modified tank model as variables instead of constants [23]. The same terminology is used in computer science for machine learning, where model parameters are tuned using an abundance of observations.…”

A Physically—Based Geometry Model for Transport Distance Estimation of Rainfall-Eroded Soil Sediment

“…Based on this fitting equation, we develop a straightforward soil erosion transport-distance model in which a geometric relationship is used to predict the maximum transport distance of soil erosion. Some physical experiments validate our physically based geometry model, providing meaningful results while using minimal historical data [11,12].…”