Groundwater Potentiality Prediction Using AHP-MCDA and GBT Model in a Typical Basement Complex, Nigeria: Insights From Remote Sensing and Geophysical Datasets

Delineation of geologic features that are capable of hosting water in economic quantity in the Basement Complex has been a major concern because they are usually localized due to restricted fractured and weathered rock. To effectively evaluate the groundwater potentiality prediction index (GPPI) accuracy of an area, solely depends on the groundwater potentiality predictors (GPPs) considered and the statistical model used in analyzing the data. Therefore, the acquired remotely sensed and geophysical depth sounding database processed using autopartial curve matching software and computer aided iteration to determine was analyzed using the conventional Analytical Hierarchy Process (AHP) model and the machine learning Gradient Boosting Tree (GBT) data driven model. Such a data driven model (GBT) is efficient in solving complex and cognitive problems in high uncertainty and complex environments. Twelve (12) groundwater potentiality predictors (GPPs) namely: Digital Elevation Model (DEM), Slope (S), Drainage Density (Dd), Land Use (Lu), Aquifer Resistivity (ρa), Aquifer Thickness (h), Overburden Thickness (b), Aquifer Hydraulic Conductivity (k), Aquifer Transmissivity (Tr), Aquifer Storativity (St), Aquifer Diffusivity (D), Aquifer Reflection Coefficient (Rc). The efficacy of GBT model was applied using the Salford Predictive Modeler 8.0 software. The data were partitioned into training and test dataset in ratio 90:10 using k-10 cross validation techniques. Their prediction importance was determined and the groundwater potentiality prediction index calculated and processed in the ArcGIS environment to produce the groundwater potential prediction index (GPPI) map of the investigated area. The investigated area was classed into three (3) zonations of low, moderate and high groundwater potential with about 56% classed within the low groundwater potential zone. Fifteen (15) water column measurement from wells was used to validate the developed model by calculating the predictive correlation accuracy (PCA) using the spearman's correlation analysis. The AHP-GPPI and GBT-GPPI model gave a correlation of (rs = 0.66; p = .007) and (rs = 0.74; p = .002) respectively. In conclusion, the model has proven that the drop in aquifer resistivity doesn't necessitate the presence of groundwater but rather several parameter should be integrated together to better understand the true nature of the aquifer.

Transmissivity and groundwater flow exert a strong influence on drainage density

The extent to which groundwater flow affects drainage density and erosion has long been debated but is still uncertain. Here, I present a new hybrid analytical and numerical model that simulates groundwater flow, overland flow, hillslope erosion and stream incision. The model is used to explore the relation between groundwater flow and the incision and persistence of streams for a set of parameters that represent average humid climate conditions. The results show that transmissivity and groundwater flow exert a strong control on drainage density. High transmissivity results in low drainage density and high incision rates (and vice versa), with drainage density varying roughly linearly with transmissivity. The model evolves by a process that is defined here as groundwater capture, whereby streams with a higher rate of incision draw the water table below neighbouring streams, which subsequently run dry and stop incising. This process is less efficient in models with low transmissivity due to the association between low transmissivity and high water table gradients. A comparison of different parameters shows that drainage density is most sensitive to transmissivity, followed by parameters that govern the initial slope and base level. The results agree with field data that show a negative correlation between transmissivity and drainage density. These results imply that permeability and transmissivity exert a strong control on drainage density, stream incision and landscape evolution. Thus, models of landscape evolution may need to explicitly include groundwater flow.

Hydrological response of hydrographic sub-basins in the Piracicaba River Basin - Southeast Region of Brazil

Use of water for several human needs, associated with climate change, indicates the need understand the response of watersheds, in order to provide adequate water resources planning and management. This study was carried out in two pairs of hydrographic watersheds, in the Piracicaba River Basin, southeast of Brazil, analyzing water response, integrating in-situ collected precipitation and flow data, natural environment attributes, and anthropic environmental data. To support the analysis, Surface Runoff Potential Charts (SRPC). The evaluation of the physical characteristics of the sub watersheds (SW(A) and SW(B)) shows that these areas present very low to low potential, indicating greater infiltration capacity. The use and coverage of the soil partially justifies the flow changes in pair 1, since SW(A) has a larger extent of agricultural areas that can use irrigation. SW(B), even with a greater variety of crops, has a smaller cultivated area and tends to demand less water. At pair 2, the low runoff potential is mainly due to the predominance of flat relief in the sub-basins. The soils that compose them present a higher fraction of silt and clay, with thicknesses > 5m in SW(C) and varying from 0.5m, reaching depths above 5m in SW(D), however, the physical properties of these soils do not provide a low flow rate, but associated with the low slope of the land, the geological characteristics and low drainage density are configured in regions where the flow flows more slowly, contributing to the evaporation and infiltration process. The use and coverage of the soil also partially justifies the flow oscillations, due to anthropic activities in SW(C) and SW(D), such as irrigation and spraying of citrus, fertirrigation of sugarcane, irrigation of seedling nurseries, directly interfering with the availability of surface water.

Drainage Density and Its Controlling Factors on the Eastern Margin of the Qinghai–Tibet Plateau

The drainage density (Dd) is an important index to show fluvial geomorphology. The study on Dd is helpful to understand the evolution of the whole hydrological and geomorphic process. Based on the Shuttle Radar Topography Mission 90-m digital elevation model, the drainage network of basins along the eastern margin of the Qinghai–Tibet Plateau is extracted using a terrain morphology-based method in ArcGIS 10.3, and Dd is calculated. The spatial characteristics of Dd are analyzed, and the relationship between Dd and its influencing factors, e.g., the topography, precipitation, and vegetation coverage, is explored. Our results show that terrains with a plan curvature ≥3 can represent the channels in the study area. Dd ranges from 2.5 to 0.1 km/km2, increases first, and then decreases from north to south on the eastern margin of the Qinghai–Tibet Plateau. Dd decreases with increasing average slope and average local relief. On the low-relief planation surfaces, Dd increases with increasing altitude, while on the rugged mountainous above planation surfaces, Dd decreases rapidly with increasing altitude. Dd first increased and then decreased with increasing mean annual precipitation (MAP) and normalized difference vegetation index (NDVI), and Dd reaches a maximum in the West Qinling Mountains with a semi-arid environment, indicating that Dd in different climatic regions of the eastern margin of the Qinghai–Tibet Plateau was mainly controlled by precipitation and vegetation.

Site selection for managed aquifer recharge in the city of Kabul, Afghanistan, using a multi-criteria decision analysis and geographic information system

While the success and sustainability of managed aquifer recharge (MAR) strongly depends on many characteristics of the site, it is necessary to integrate the site characteristics and develop suitability maps to indicate the most suitable locations. The objective of this study is to integrate geographic information system (GIS) and multi-criteria decision analysis (MCDA) techniques to identify the most suitable areas for a MAR project in the Kabul city area, Afghanistan. Data for six effective criteria, including slope, drainage density, surface infiltration rate, unsaturated zone thickness, soil type and electrical conductivity, were collected and then a classification map was produced for each criterion in the GIS environment. By applying MCDA techniques, the weights of the effective criteria were obtained. A suitability map was generated from each technique separately based on a combination of all criteria weights and thematic layers. The result of the analytical network process (ANP) method was found to be more precise and reliable compared with that of the analytical hierarchy process (AHP) method. Based on the final suitability map produced from the ANP model, there is 3.7, 15.0, 37.4, 33.1 and 10.3% of the total area that is unsuitable, of low suitability, moderately suitable, suitable and very suitable for MAR application, respectively. As a final result of this work, seven sites have been prioritized based on land use. The integration of multi-criteria decision analysis and GIS is recognized as an effective method for the selection of managed aquifer recharge sites.

Demarcation of Groundwater Potential Zones Using Geospatial Technology in Edappadi Block, Salem District, Tamil Nadu, India

Groundwater is one of the world's most valuable resources, which contributes 85% of drinking water supplies. It is imperative to explore ground-water zone for the utilization to the people. Edappadi block in Salem District, Tamil Nadu, is rocky terrain largely depends on groundwater for drinking and irrigation. One of the most useful tools for locating ground water potential zones is remote sensing and geographic information system (GIS). Different types of thematic maps, such as lithology, geomorphology, drainage density, slope, lineament, and land use/land cover, can be easily created by visual interpretation of IRS-1C, LISS-III data and maps are prepared using GIS. The water potential zones are determined using a rank and weightage approach. In order to demarcate the water potential zones, the vector overlay method is used. Lithology is given more weight than geomorphology, followed by lineament density, lineament frequency, lineament intersection, slope and land use/land cover. Based on the overall results, the potential zone of groundwater in the research region is divided into five groups: Excellent, Very Good, Good, Moderate, and Poor.

Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

Assessment of groundwater potential zones using GIS and AHP techniques: a case study of the Lafia district, Nasarawa State, Nigeria

In the Lafia district, rising population has increased the need for groundwater resources for economic growth. Sustainable groundwater resource management demands accurate quantitative assessment, which may be accomplished using scientific theories and innovative methods. In present study, an integrated method has been employed to assess the groundwater potential zones in the Lafia district utilizing remote sensing (RS), geographic information system (GIS), and analytic hierarchy method (AHP). For this aim, eight thematic maps regulating to occurrence and transportation of groundwater (i.e., geology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover and lineament density) were generated and converted into raster format utilizing ArcGIS tool. Weights were assigned to these eight thematic maps based on their importance. Moreover, the final normalized weights of these parameters were calculated adopting pairwise comparison matrix of the AHP. To create the groundwater potential zones (GWPZs) map of the research area, we employed the overlay weighted sum approach to combine the parameters. The map has been divided into four zones (good, moderate, poor and very poor), each of which represents 19.3, 12.9, 57.8, and 10% of the study area. Lastly, the GWPZs map was validated utilizing borehole data obtained from 50 wells scattered throughout the study area to examine the performance of the approach. The validation results demonstrate that the adopted procedure produces highly reliable results that can aid in long-term development and strategic use of groundwater resources in this area.

Methodology for mapping the probability of fire occurrence in the Brazilian cerrado biome based on the danger of fire propagation variables

The area covered by the Brazilian cerrado biome has been greatly reduced in recent years due to the expansion of agricultural land and the increased number of fire outbreaks. The objective of this paper is to propose a methodology based on geospatial analysis and logistic regression analysis (LRA) for mapping the probability of fire occurrence in Brazilian cerrado conservation units. This model was applied in the Serra da Canastra National Park (SCNP) in the Southeast of Brazil. The methodology uses the maps of the following environmental variables, which are related to the danger of fire propagation: wind effect (WIN), terrain convexity (CVX), slope (SLO), drainage density (DRD), altitude (ELV), vegetation index (NDVI), and road density (ROD). The results of the LRA showed that the variables SLO, ELV, NDVI, ROD (p<0.0001), DRD (p=0.0005) and WIN (p=0.0007) contributed significantly to the occurrence of fire outbreaks. The model correctly classified 94.26% of cases. We conclude that this methodology can be used to inform the planning of firefighting actions in the Brazilian cerrado biome.

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.