An Integration of Spatial Information Technology for Groundwater Potential and Quality Investigations in Koduvan Ár Sub-Watershed of Meenachil River Basin, Kerala, India

Rekha, V.B.; Thomas, A. P.; Suma, M.; Vijith, H.

doi:10.1007/s12524-010-0050-6

Cited by 22 publications

(9 citation statements)

References 34 publications

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“…Groundwater Potential. Lithology [22] influences the water-holding capacity of aquifer and directly affects the occurrence and distribution of groundwater. The lithology thematic map was derived through digitizing the 1 : 250,000 scale geology map from the National Geological Library as shown in Figure 3.…”

Section: Factors Related Tomentioning

confidence: 99%

Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms

Duan

Deng

et al. 2016

Mathematical Problems in Engineering

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Groundwater plays an important role in global climate change and satisfying human needs. In the study, RS (remote sensing) and GIS (geographic information system) were utilized to generate five thematic layers, lithology, lineament density, topology, slope, and river density considered as factors influencing the groundwater potential. Then, the multicriteria decision model (MCDM) was integrated with C5.0 and CART, respectively, to generate the decision tree with 80 surveyed tube wells divided into four classes on the basis of the yield. To test the precision of the decision tree algorithms, the 10-fold cross validation and kappa coefficient were adopted and the average kappa coefficient for C5.0 and CART was 90.45% and 85.09%, respectively. After applying the decision tree to the whole study area, four classes of groundwater potential zones were demarcated. According to the classification result, the four grades of groundwater potential zones, "very good," "good," "moderate," and "poor," occupy 4.61%, 8.58%, 26.59%, and 60.23%, respectively, with C5.0 algorithm, while occupying the percentages of 4.68%, 10.09%, 26.10%, and 59.13%, respectively, with CART algorithm. Therefore, we can draw the conclusion that C5.0 algorithm is more appropriate than CART for the groundwater potential zone prediction.

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Section: Factors Related Tomentioning

confidence: 99%

Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms

Duan

Deng

et al. 2016

Mathematical Problems in Engineering

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“…[139] found the best accuracy in mapping impervious surfaces occurred when statistical tools (e.g., Impervious Surface Analysis Tool) combined remotely sensed data sources with population (e.g., US census data). Runoff models using impervious surface mapping have led to improved understanding of groundwater quality and recharge [144][145][146], floodplain management, and coastal flooding [147][148][149].…”

Section: Urban Hydrologymentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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“…GIS has a wide range of application in different aspects including national security and fuzzy territorial border conflict [2]; underground water quality, water measurement and urban water supply service [3]; observing the physiological activity of a population and taking actions to protect endangered species [4], etc. Implementing GIS into reservoir resettlement will definitely improve the efficiency of migration management work as well as ensure the smooth process of resettlement project.…”

Section: Introductionmentioning

confidence: 99%

Research on GIS Based Reservoir Resettlement Data Management System — An Example of Guangdong Reservoir Resettlement Project

Shen¹

2017

JCP

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Reservoir resettlement management is the fundamental project to the success of construction of reservoir. Due to the diversity of types of engineering data and trivial content as well as wide range of covers, modern technologies like GIS are required to build the information system and to achieve systematic management of data which can be displayed through diverse methods in order to satisfy requirements of people from different backgrounds. This paper introduced the application module of reservoir management system and gave an example of practical application in Guangdong Province, aiming at demonstrating high effectiveness, systematicness and convenience of modern technologies in reservoir resettlement project management.

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An Integration of Spatial Information Technology for Groundwater Potential and Quality Investigations in Koduvan Ár Sub-Watershed of Meenachil River Basin, Kerala, India

Cited by 22 publications

References 34 publications

Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms

Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Research on GIS Based Reservoir Resettlement Data Management System — An Example of Guangdong Reservoir Resettlement Project

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