Wetness index based on landscape position and topography (WILT): Modifying TWI to reflect landscape position

Bitew, M. M.; Younger, Seth E.; Jackson, C. Rhett; Du, E.; Drover, Damion R.

doi:10.1016/j.jenvman.2019.109863

Cited by 41 publications

(21 citation statements)

References 44 publications

(56 reference statements)

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“…Environmental and topographic variables were chosen after performing a literature review that indicated their relevance for the occurrence of both malaria and vectors [12,17,31]. These included the Normalized Difference Vegetation Index (NDVI), which determines how much near‐infrared light is reflected compared to visible red and helps to evaluate vegetation conditions or to differentiate bare soil from grass or forest [32]; the Normalized Difference Water Index (NDWI) used for assessing the presence of moisture in vegetation cover; changes in NDWI values reflect either sufficient vegetation water content or water stress [33], and the Topographic Wetness Index (TWI), which predicts relative surface wetness; it is an indicator of places where water will tend to accumulate [34]. The variables were obtained from various databases (Table 1), processed in raster format of 1 km 2 resolution and filtered with a mask for the Pacific region.…”

Section: Methodsmentioning

confidence: 99%

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

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Objective To categorise and map, at high resolution, the risk of malaria incidence in the Pacific region, the main malaria‐endemic region of Colombia. Methods The relationship between the environmental variables Normalized Difference Vegetation Index Normalized Difference Water Index, Topographic Wetness Index, precipitation and temperature with the observed Annual Parasitic Index was evaluated using a generalised linear model. An incidence risk map at a resolution of 1 km2 was constructed and projected to the entire endemic region. Associations of malaria risk categories with both presence records and co‐occurrence of the three main malaria vectors were determined. Results A significant correlation was found for the incidence of malaria with precipitation and Normalized Difference Vegetation Index (R2 = 0.98, P < 0.05), whereas there was no significant correlation with the remaining environmental and topographic variables. Moderate‐ to high‐risk areas were located mainly in central Chocó Department along the San Juan and Atrato rivers and in areas west of the Cauca River and Pacific lowlands of the Andes Mountains. There was a statistically significant relationship for the presence of the two main vectors Anopheles darlingi and Anopheles nuneztovari with the high malaria risk category. Furthermore, malaria risk was directly proportional to the number of co‐occurring vector species. Conclusions The map obtained provides useful information on the risk of malaria in particular places of the Colombian Pacific region. The data can be used by public entities to optimise the allocation of economic resources for vector control interventions and surveillance.

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Section: Methodsmentioning

confidence: 99%

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

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“…The curvature value of study area lied in five intervals: 0.5485 -20.159, 0.014878 -0.54849, -0.25192 -0.014877, -0.65213 --0.25193, -13.859 --0.65214. The topographic wetness index ascertained the spatial wetness status of the basin area, which affects the occurrence of floods in the region (Meles et al 2020). TWI was calculated as follows:…”

Section: Flood Conditioning Factors and Database Generationmentioning

confidence: 99%

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Yaseen

Chen

2022

Stoch Environ Res Risk Assess

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Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flooding are different for every region as they depend upon the characteristics of each region. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of the ensemble model showed 99% accuracy in training and 98% accuracy in validation datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

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“…Topographic wetness index is a term that is frequently utilized to illustrate how topography affects the location and length of saturated source areas [92]. It depicts how topography influences runoff generation and the volume of flow that accumulates in a basin.…”

Section: Topographic Wetness Indexmentioning

confidence: 99%

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Mallick

Talukdar

Kahla

et al. 2021

Water

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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.

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Wetness index based on landscape position and topography (WILT): Modifying TWI to reflect landscape position

Cited by 41 publications

References 44 publications

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

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