DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

Nguyen, Kieu Anh; Chen, Walter

doi:10.3390/ijgi10070452

Cited by 14 publications

(10 citation statements)

References 38 publications

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“…By and large, the northern portion of the watershed, which is adjacent to the reservoir, experiences less soil erosion than the southern portion of the watershed. This corresponds to the forecast made by machine learning models using erosion pin methods [34]. Deposition, on the other hand, is more common around natural depressions, roadways, and riverbanks.…”

Section: Resultsmentioning

confidence: 66%

Soil Erosion and Deposition in a Taiwanese Watershed Using USPED

2022

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Soil erosion is a global problem that has been exacerbated in recent decades by global warming and the increased frequency of extreme weather events. It is also a global issue addressed by the United Nations’ Sustainable Development Goal #15 that seeks to recover degraded land and create a world free of land degradation by 2030. In this study, we used the Unit Stream Power-based Erosion Deposition (USPED) model to investigate the distribution of soil erosion and deposition in an important reservoir watershed in Taiwan, which is known to have a high risk of sediment hazard. We found the average soil erosion rate to be 136.4 Mg/ha/year using the model’s recommended m = 1.3 and n = 1.2 empirical coefficients for a combined occurrence of sheet and rill erosion. Additionally, we selected the Sule sub-watershed and the Kala area as examples to illustrate the pattern of soil erosion and deposition and their relationship to rivers, roadways, and anthropogenic activity, and 3D terrain was employed to further enhance visualization of the model output. It was estimated that 12.6% of eroded soil was deposited with a 200 m buffer of the rivers in the watershed and might be swept into the river system by the next typhoon, torrential rain, landslide, or earthquake. In comparison to previous USLE- and RUSLE-based soil erosion modeling in the same research area, our USPED modeling is unique in that it included the amount and distribution of soil deposition. This successful implementation of USPED in Taiwan establishes a new modeling alternative in addition to the widely used USLE and RUSLE models. The findings can be used to direct future erosion pin placement in the research area, allowing for improved monitoring of sediment movement and avoiding sediment hazards.

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Section: Resultsmentioning

confidence: 66%

Soil Erosion and Deposition in a Taiwanese Watershed Using USPED

2022

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“…Austria has the highest mean LS factor among the EU countries and the UK, at 6.95, which is 91 percent of Taiwan's. This indicates the importance of the LS factor in soil loss and echoes Taiwan's high soil erosion rates as measured by erosion pins [26,27].…”

Section: Discussionmentioning

confidence: 63%

The New Island-Wide LS Factors of Taiwan, with Comparison with EU Nations

Chen

Nguyen

2022

Sustainability

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Soil erosion is a global environmental challenge that the United Nations Sustainable Development Goal (UN SDG) #15 wants to address, and the topographic factor, according to the RUSLE (Revised Universal Soil Loss Equation) model, is one of the most critical factors causing soil erosion. In this study, we employed three separate digital elevation models of Taiwan, with horizontal resolution ranging from 20 to 90 m, to compute the LS factors based on the upslope contributing areas and multiple flow directions, utilizing the methodologies used by the European Soil Data Centre. This is the first study to create a map of Taiwan’s island-wide LS factors without using a fixed slope length of 40 m. To compare European Union countries with Taiwan, we also calculated their LS means, standard deviations, and coefficients of variation of LS factors. As a result, Taiwan’s high LS values are readily noticeable as compared to the EU. Taiwan’s LS factor is greater than that of any EU country and the United Kingdom, at 2.69 times the EU average. To put it another way, while all other erosive factors are held equal, Taiwan’s average soil erosion is about 2.69 times that of the EU. With an LS factor of 6.95, Austria has the highest average LS in the EU, yet it is 91 percent of Taiwan’s. The findings demonstrate that Taiwan has a far higher mean LS factor than any EU country or the United Kingdom, which helps to partially explain why soil erosion in Taiwan is substantially higher than in the EU.

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“…We assessed a wide array of machine learning methodologies, including multiple regression, decision trees, support vector machines (SVMs), adaptive neuro-fuzzy inference systems, artificial neural networks (ANNs), bagged multivariate adaptive regression splines, Cubist, and gradient boosting machines (GBMs), alongside a stacked ensemble approach integrating RF and GBM as meta-models with decision trees, linear regression, ANN, and SVM as base models. These methods were rigorously compared in a series of studies [13,[28][29][30][31], which led us to identify RF as a superior model for our specific analytical needs. Thus, we re-applied the random forest model to forecast the NDVI using climate and topographic variables in this study.…”

Section: Methodsmentioning

confidence: 99%

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

Nguyen,

Seeboonruang,

Chen

2023

Environments

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In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.

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DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

Cited by 14 publications

References 38 publications

Soil Erosion and Deposition in a Taiwanese Watershed Using USPED

Soil Erosion and Deposition in a Taiwanese Watershed Using USPED

The New Island-Wide LS Factors of Taiwan, with Comparison with EU Nations

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

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