A Topographic Wetness Index for Forest Road Quality Assessment: An Application in the Lakeland Region of Finland

Waga, Katalin; Malinen, Jukka; Tokola, Timo

doi:10.3390/f11111165

“…It also determines the effect of topography on the level of saturation surface to produce runoff [41]. TWI calculates the probability of flow accumulation in soil due to the upstream catchment area and slope [42]. Thus, TWI was applied as a topographical variable for modeling gully erosion.…”

Section: Topographic Wetness Indexmentioning

confidence: 99%

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Ahmadpour

¹

,

Bazrafshan

²

,

Rafiei-Sardooi

³

et al. 2021

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Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

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“…In addition to the factors mentioned above, the susceptibility for contamination depends on the groundwater flow pattern and the concatenated soil moisture distribution (Waga et al 2020). The latter is encoded within the TWI index (Fig.…”

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

Rafiei-Sardooi

¹

,

Ghazanfarpour

²

,

Azareh

³

et al. 2023

Preprint

1

0

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Groundwater contamination risk mapping is one essential measure in groundwater management and quality control. The purpose of the present study is to address this mapping by means of a novel framework, which is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an Analytic Hierarchy Process (AHP). However, to obtain the risk map, the results about groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling based, in part, on the occurrence probability predicted from Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Support Vector Machine (SVM). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of the impacts of the contamination based on socio-environmental variables, and is particularly suitable for applications based on limited amount of available data. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, which is associated with high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands. Therefore, our work is providing new modeling insights for the future assessment of groundwater contamination, with potential impacts for the management and control of water resources in arid and semi-arid environments.

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“…4f). Moreover, the topographic wetness index (TWI) encodes information on the spatial pattern of groundwater ow and soil moisture (Waga et al 2020). The TWI map for Jiroft plain was obtained by means of digital elevation model (DEM) with a cell size of 30 m × 30 m in SAGA GIS (Fig.…”

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

Rafiei-Sardooi

¹

,

Ghazanfarpour

²

,

Azareh

³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

Groundwater contamination risk mapping is one essential measure in groundwater management and quality control. The purpose of the present study is to address this mapping by means of a novel framework, which is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an Analytic Hierarchy Process (AHP). However, to obtain the risk map, the results about groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling based, in part, on the occurrence probability predicted from Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Support Vector Machine (SVM). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of the impacts of the contamination based on socio-environmental variables, and is particularly suitable for applications based on limited amount of available data. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, which is associated with high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands. Therefore, our work is providing new modeling insights for the future assessment of groundwater contamination, with potential impacts for the management and control of water resources in arid and semi-arid environments.

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A Topographic Wetness Index for Forest Road Quality Assessment: An Application in the Lakeland Region of Finland

Cited by 10 publications

References 22 publications

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

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