/pdf/qualitative-research-in-information-systems-1sezxwy02d.pdf

Qualitative Research in Information Systems

Shallow groundwater affects terrestrial ecosystems by sustaining river base-flow and root-zone soil water in the absence of rain, but little is known about the global patterns of water table depth and where it provides vital support for land ecosystems We present global observations of water table depth compiled from government archives and literature, and fill in data gaps and infer patterns and processes using a groundwater model forced by modern climate, terrain, and sea level Patterns in water table depth explain patterns in wetlands at the global scale and vegetation gradients at regional and local scales Overall, shallow groundwater influences 22 to 32% of global land area, including ~15% as groundwater-fed surface water features and 7 to 17% with the water table or its capillary fringe within plant rooting depths

http://science.sciencemag.org/content/sci/339/6122/940.full.pdf

Global patterns of groundwater table depth.

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

Remote sensing (RS) and geographic information system (GIS) are promising tools for efficient planning and management of vital groundwater resources, especially in data-scarce developing nations. In this study, a standard methodology is proposed to delineate groundwater potential zones using integrated RS, GIS and multi-criteria decision making (MCDM) techniques. The developed methodology is demonstrated by a case study in Udaipur district of Rajasthan, western India. Initially, ten thematic layers, viz., topographic elevation, land slope, geomorphology, geology, soil, pre- and post-monsoon groundwater depths, annual net recharge, annual rainfall, and proximity to surface water bodies were considered in this study. These thematic layers were scrutinized by principal component analysis technique to select influential layers for groundwater prospecting. Selected seven thematic layers and their features were assigned suitable weights on the Saaty’s scale according to their relative importance in groundwater occurrence. The assigned weights of the thematic layers and their features were then normalized by using AHP (analytic hierarchy process) MCDM technique and eigenvector method. Finally, the selected thematic maps were integrated by weighted linear combination method in a GIS environment to generate a groundwater potential map. Thus, four groundwater potential zones were identified and demarcated in the study area, viz., ‘good’, ‘moderate’, ‘poor’ and ‘very poor’ based on groundwater potential index values. The area falling in the ‘good’ zone is about 2,113 km2 (17% of the total study area), which encompasses major portions of Sarada, Salumber, Girwa, Dhariawad, and Mavli blocks of the study area. The northeast and southwest portions along with some scattered patches fall in the ‘moderate’ zone, which encompasses an area of 3,710 km2 (about 29%). The ‘poor’ zone is dominant in the study area which covers an area of 4,599 km2 (36% of the total area). The western portion and parts of eastern and southeast portions of the study area are characterized as having ‘very poor’ groundwater potential, and this zone covers an area of 2,273 km2 (18%). Moreover, in the ‘good’ zone, the mean annually exploitable groundwater reserve is estimated at 0.026 million cubic metres per km2 (MCM/km2), whereas it is 0.024 MCM/km2 in the ‘moderate’ zone, 0.018 MCM/km2 in the ‘poor’ zone, and 0.013 MCM/km2 in the ‘very poor’ zone. The groundwater potential map was finally verified using the well yield data of 39 pumping wells, and the result was found satisfactory.

Assessment of Groundwater Potential in a Semi-Arid Region of India Using Remote Sensing, GIS and MCDM Techniques

Multi-criteria decision analysis (MCDA) as an advantageous tool has been applied by various researchers to improve their management ability. Management of groundwater resource, especially under data-scarce and arid areas, encountered a lot of problems and issues which drives the planers to use of MCDA. In this research, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran. At first, the effective thematic layers on the groundwater potential such as rainfall, lithology, drainage density, lineament density, and slope percent were derived from the spatial geodatabase. Then, the assigned weights of thematic layers based on expert knowledge were normalized by eigenvector technique of AHP. To prepare the groundwater potential index, the weighted linear combination (WLC) method was applied in GIS. Finally, the receiver operating characteristic (ROC) curve was drawn for groundwater potential map, and the area under curve (AUC) was computed. Results indicated that the rainfall and slope percent factors have taken the highest and lowest weights, respectively. Validation of results showed that the AHP method (AUC = 73.66 %) performed fairly good predication accuracy. Such findings revealed that in the regions suffering from data scarcity through the MCDM methodology, the planners would be able to having accurate knowledge on groundwater resources based on geospatial data analysis. Therefore, the developing scenario for future planning of groundwater exploration can be achieved in an efficient manner.

Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS

The groundwater recharge zone mapping often requires a large amount of spatial information and criteria. Geographic information systems are capable of managing large amount of spatially related information, providing the ability to integrate multiple layers of information for multi-criteria analysis. To show the capabilities of GIS techniques for mapping groundwater refill zone in arid area, a study was carried out in the Maknassy basin located in Central Tunisia. This evaluation incorporates historic rainfall data analysis, watershed drainage density, surficial geology and aquifer boundary conditions. The study basin is categorized according to the previous criteria. Multi-criteria analysis is performed to evaluate suitability to the groundwater recharge for each factor, according to its associated weight. The thematic layers were integrated with one another using the weighted aggregation method to derive the groundwater recharge map. The results demonstrated that the GIS methodology has good functionality for mapping groundwater recharge zone.

Groundwater Recharge Zone Mapping Using GIS-Based Multi-criteria Analysis: A Case Study in Central Tunisia (Maknassy Basin)

Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling

Seawater intrusion and associated processes: Case of the Korba aquifer (Cap-Bon, Tunisia)

Geophysical and hydrochemical study of the seawater intrusion in Mediterranean semi arid zones. Case of the Korba coastal aquifer (Cap-Bon, Tunisia)

Purpose
Approximately 74 % of agricultural soils in Tunisia are affected by water erosion, leading to the siltation of numerous human-made reservoirs and therefore a loss of water storage capacity. The objective of this study was to propose a methodology for estimating the relative contributions of gully/channel bank erosion and surface topsoil erosion to the sediment accumulated in small reservoirs.

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Abdallah Ben Mammou

Papers

Groundwater Recharge Zone Mapping Using GIS-Based Multi-criteria Analysis: A Case Study in Central Tunisia (Maknassy Basin)

Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling

Seawater intrusion and associated processes: Case of the Korba aquifer (Cap-Bon, Tunisia)

Geophysical and hydrochemical study of the seawater intrusion in Mediterranean semi arid zones. Case of the Korba coastal aquifer (Cap-Bon, Tunisia)

Fingerprinting sediment sources in the outlet reservoir of a hilly cultivated catchment in Tunisia