Soil is one of the main elements of natural resources. Accurate estimation of soil erosion is very important in optimum soil resources development and management. Analyzing soil erosion by water on cultivated lands is an important task due to the numerous problems caused by erosion. In this study, the performance of three different data-driven approaches, e.g. multilayer perceptron artificial neural network (ANN), grid partitioning (GP), and subtractive neuro-fuzzy (NF) models were evaluated for estimating soil erosion. Land use, slope, soil and upland erosion amount were used as input parameters of the applied models and the erosion values obtained by MPSIAC method were considered as the benchmark for evaluating the ANN and NF models. The applied models were assessed using the coefficient of determination (R2), the root mean square error (RMSE), the BIAS, and the variance accounted for (VAF) indices. The results showed that the subtractive NF model presented the most accurate results with the minimum RMSE value (3.775) and GP, NF and ANN models were ranked successively.
Although plot-scale erosion experiments are numerous, there are few studies on constructed landforms. This limits the understanding of their long-term stability, which is especially important for planning mined land rehabilitation. The objective of this study was to gain insight into the erosion processes in a 30 × 30 m trial plot on a mine waste rock dump in tropical northern Australia. The relationships between rainfall, runoff and suspended and bedload sediment export were assessed at annual, seasonal, inter-event and intra-event timescales. During a five-year study period, 231 rainfall–runoff–sediment export events were examined. The measured bedload and suspended sediments (mainly represented in nephelometric turbidity units (NTU)) showed the dominance of the wet season and heavy rainfall events. The bedload dominated the total mass, although the annual bedload diminished by approximately 75% over the five years, with greater flow energy required over time to mobilise the same bedload. The suspended load was more sustained, though it also exhibited an exhaustion process, with equal rainfall and runoff volumes and intensities, leading to lower NTU values over time. Intra-event NTU dynamics, including runoff-NTU time lags and hysteretic behaviours, were somewhat random from one event to the next, indicating the influence of the antecedent distribution of mobilisable sediments. The value of the results for supporting predictive modelling is discussed.
<p>Extensive disturbances during the mining and rehabilitation process can include removal of vegetation, removal and storage of soils hence their modification, changes in topography, and planting of new vegetation. A main goal of mine rehabilitation is to produce a post-mining landscape that is resistant to geotechnical failure and to surface erosion processes. To achieve this, hydrology and erosion models are required to determine erosion rates under alternative landscape designs, including landscape form and cover options.</p> <p>By critical review of the relevant literature, it was found that most previous erosion modelling studies have concentrated on surface hydrology in agricultural, forestry, and other natural systems, while disturbed ecosystems like mining regions have received little attention. Landscape evolution models have been developed for mined landform applications but modelling over long time-scales compromises the temporal and spatial resolution.</p> <p>The main objectives of this research therefore were:</p> <ul> <li>Extend an existing plot-scale hydrological model to plot-scale erosion model.</li> <li>To improve knowledge of the errors and uncertainty in applying a high-resolution erosion model to mined landforms and to conclude on the potential applicability and limitations of EroCA.</li> </ul> <p>The experimental data used in the research were from a 30 m &#215; 30 m field plot on a mine waste rock dump in the wet tropical environment of the Ranger mine (north-east Australia) from the period 2009 to 2014. The new EroCA model is an extension to the RunCA model, which was developed to provide high resolution simulation of runoff and infiltration in constructed landforms. The extended model uses mass balance principles and established erosion and sediment transport models, covering both suspended and bedload, and solves the equations using the cellular automata approach. Code verification against analytical solutions of runoff and sediment illustrated small errors, which were partly due to approximations used in the analytical solutions. The EroCA model was then applied to the Ranger experimental plot data to assess the suspended and bedload erosion performance. EroCA was able to reasonably represent the observed flows and turbidity profiles. Although an arbitrary reduction in the erodibility parameter value of 20% per year was needed to simulate the bedload depletion.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.