Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

Özdemir, Adnan

doi:10.1016/j.jhydrol.2011.05.015

Cited by 254 publications

(114 citation statements)

References 57 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…To avoid multicollinearity amongst the independent variables, a multicollinearity test was conducted. For this purpose, the variance inflation factor (VIF) analysis [29,67] was used. Based on VIF analyses, each independent variable (factor) was linearly regressed against other independent variables (factors).…”

Section: Analysis Of Driving Forces Of Cropland Changementioning

confidence: 99%

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

2018

View full text Add to dashboard Cite

Abstract:To cope with the growing agrarian crises in Afghanistan, the government (following the fall of the Taliban regime in 2002) has taken measures through cropland expansion "extensification" and switching to mechanized agriculture "intensification". However, cropland expansion, on one hand, disturbs the existing land use/cover (LULC) and, on other hand, many socio-economic and biophysical factors affect this process. This study was based on the Kabul River Basin to answer two questions: Firstly, what was the change in LULC since 2001 to 2010 and, secondly, what are the drivers of cropland change. We used the spatial calculating model (SCM) for LULC change and binomial logistic regression (BLR) for drivers of cropland change. The net change shows that cropland, grassland, water-bodies, and built-up areas were increased, while forest, unused, and snow/ice areas were decreased. Cropland was expanded by 13%, which was positively affected by low and plain landforms, slope, soil depth, investment on agriculture and distance to the city, while it was negatively affected by plateaus and hill landforms, dry semi-arid, moist semi-arid, and sub-humid zones, precipitation, population, and the distance to roads and water. Climate adaptation measures, cropland protection in flood prone zones, population and rural migration control, farmer access to credit, irrigation, and inputs are necessary for agricultural deployment.

show abstract

Section: Analysis Of Driving Forces Of Cropland Changementioning

confidence: 99%

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

2018

View full text Add to dashboard Cite

show abstract

“…Moreover, it explains the relative contribution of each predictor to the response, controlling for the influences of the rest of the predictors in the model 22,23 . Additionally, logistic regression establishes a functional relationship between the binary coded preferences for arsenic-mitigation technology and the predictors that are recognized as players shaping the decisionmaking to prefer a specific arsenic-mitigation technology 25,26 . For the current study, we applied the Binary Logistic Regression Model (BLRM) technique, as the responses were dichotomous 22,26,27 .…”

Section: Statistical Analysis and Modellingmentioning

confidence: 99%

Developing Sustainable Models of Arsenic-Mitigation Technologies in the Middle-Ganga Plain in India

Singh

Taylor

2017

Current Science

View full text Add to dashboard Cite

This study seeks to understand factors that guide the decision-making process to adopt and implement the available arsenic-mitigation technologies in rural areas in the middle-Ganga Plain in India. A total of 340 households comprising 2500 people were surveyed. Socio-economic and demographic factors, water and sanitation status, time spent and distance travelled to collect water, arsenic awareness, willingness to pay (WTP) for arsenic-free water, people's trust in others and in institutions, social capital in communities, and preferences for sustainable arsenic-mitigation options were investigated. Arsenic treatment units (filters) and piped water supply systems were the most preferred sustainable arsenic-mitigation options in the surveyed villages. Less preferred arsenic-mitigation options include deep tube wells, dug wells, and rainwater harvesting systems. Binary logistic regression models for each arsenic-mitigation option were produced. Arsenic awareness, WTP, trust in agencies, trust in institutions and social capital were found to be the most significant factors for decision-making for preferring one arsenic-mitigation technology over the others. We recommend a mixed model of two arsenicmitigation options for the studied individuals, which could be a sustainable arsenic-mitigation option for them, considering their socio-economic and demographic conditions. Existing institutions should be strengthened, agencies empowered, and communities enlightened about arsenic problems.

show abstract

“…Many studies implemented frequency ratio method (Ozdemir 2011a,Mohammady et al 2012Ozdemir and Altural 2013), other studies described probabilistic model's application (Murthy and Mamo 2009;Oh HJ et al 2011). Logistic regression approach also was a frequently used method (Ozdemir 2011b). Basically these studies divided the drilling wells data into 2 parts, one part was used for training, and the other part was used for validation.…”

Section: Weight Assignmentmentioning

confidence: 99%

Extract relevant features from DEM for groundwater potential mapping

Liu

Yan

Zhai

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Multi-criteria evaluation (MCE) method has been applied much in groundwater potential mapping researches. But when to data scarce areas, it will encounter lots of problems due to limited data. Digital Elevation Model (DEM) is the digital representations of the topography, and has many applications in various fields. Former researches had been approved that much information concerned to groundwater potential mapping (such as geological features, terrain features, hydrology features, etc.) can be extracted from DEM data. This made using DEM data for groundwater potential mapping is feasible. In this research, one of the most widely used and also easy to access data in GIS, DEM data was used to extract information for groundwater potential mapping in batter river basin in Alberta, Canada. First five determining factors for potential ground water mapping were put forward based on previous studies (lineaments and lineament density, drainage networks and its density, topographic wetness index (TWI), relief and convergence Index (CI)). Extraction methods of the five determining factors from DEM were put forward and thematic maps were produced accordingly. Cumulative effects matrix was used for weight assignment, a multi-criteria evaluation process was carried out by ArcGIS software to delineate the potential groundwater map. The final groundwater potential map was divided into five categories, viz., non-potential, poor, moderate, good, and excellent zones. Eventually, the success rate curve was drawn and the area under curve (AUC) was figured out for validation. Validation result showed that the success rate of the model was 79% and approved the method's feasibility. The method afforded a new way for researches on groundwater management in areas suffers from data scarcity, and also broaden the application area of DEM data.

show abstract

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

Cited by 254 publications

References 57 publications

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

Developing Sustainable Models of Arsenic-Mitigation Technologies in the Middle-Ganga Plain in India

Extract relevant features from DEM for groundwater potential mapping

Contact Info

Product

Resources

About