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Effects of Environmental Agents of Denudation on Wetlands - A Tobit Regression Model Analysis
Abass Adeniyi Gazal1, Carol J Miller1
1Wayne State University College of Engineering.
Abstract:
Wetlands are vital ecosystems that play critical roles in biodiversity conservation, water purification, and climate regulation. However, they are increasingly vulnerable to environmental agents of denudation such as erosion, deforestation, and land-use change. Researchers have established the relationship between environmental agents, water-holding bodies, and hydrological cycle. It is imperative to state that this study intends to provide a quantitative comparison. Thus, this study employs a Tobit regression model to examine the effects of these agents on wetland size, using data from 51,885 observations and accounting for the censored nature of the wetland extent variable (Wetlevel). The model results indicate that several environmental factors significantly affect wetland size. Specifically, vegetation cover, soil moisture, and conservation efforts (erosion severity, conservation program presence, land cover heterogeneity) are positively associated with larger wetlands, while deforestation (deforestation rate) and erosion index exhibit strong negative association. Notably, erosion index had one of the most substantial negative coefficients (-0.00100, p < 0.01), confirming it is a major driver of wetland loss. The model also found that climatic variables (rainfall, temperature variability, elevation, distance to urban center) and socio-economic indicators (farming intensity, population density, land use pressure index) significantly influence wetland dynamics. The Tobit model's fit statistics (AIC/N = 2.689; ANOVA-based fit = 0.0159) affirm its suitability for modeling censored ecological data. These findings highlight the urgent need for integrated environmental management strategies targeting erosion control, forest conservation, and sustainable land use to protect and restore wetlands.
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In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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