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Groundwater is one of the major resources to supply the agriculture and urban water demand. Vulnerability of groundwater resources due to chemical substances is a crucial concern for groundwater quality management. The different nitrogen compounds, especially nitrate, plays an important role in groundwater quality. In last two decades, the efficient approaches called soft computing (SC) models were used for assessing the groundwater pollution. This study aims to assess the applications of various SC models for simulating the groundwater pollution due to nitrate contamination. In this way, the past trends and current applications of those models and essential factors required for assessing the ground water quality are demonstrated. Ultimately, several research gaps and possible future research direction are proposed.

Defining vulnerability and identifying vulnerable areas and populations is critical to climate adaptation and resilience. Neighbourhoods are not homogeneous in terms of their socio-economic and phy...

An estimated 2.7 million septic systems in Florida, USA are potential ground and surface water contaminant sources that may affect environmental and human health. This study examined the spatial di...

With continued population growth and increasing use of fresh groundwater resources, protection of this valuable resource is critical. A cost effective means to assess risk of groundwater contaminat...

The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.

This vision paper discusses future directions and existing gaps in integrating smart city initiatives with resilience frameworks. It proposes the use of a multi-modular crowdsourced Community Resiliency Information System (CRIS) to overcome traditional smart citiesâAZ focus on infrastructure and grid vulnerabilities/resiliency while overlooking socio-economic vulnerabilities. CRIS is conceptualized based on our previous research which identified the importance of customized information and targeted resources to foster preparedness, adaptation and resiliency among diverse communities. Our proposed vision of a smart city integrated with CRIS allows scalable and customizable solutions for policymakers using information generated âAŸby the peopleâAZ, thus ensuring participation of diverse communities in smart city technology, thus creating a Holistic Smart City (HSC).CRIS will foster a two-way communication between government and communities by creating a grassroots, community-based, technology-enhanced needs assessment and disaster-response information system. Among other benefits, CRIS will generate and organize data to be used by the local community and policy makers and foster ongoing dialogue between neighborhoods and policymakers. CRIS will foster social capital at the neighborhood level by increasing grassroots knowledge and access to resources and information, fostering preparedness, adaptation, and resiliency/recovery, and aiding decision-makers in resource allocation and customized communications. CRIS moves the smart city beyond a mere infrastructure to create an interactive space for information exchange, democratic participation and a collaborative resilience-building process.

This study examines the effectiveness of a crowdsourced participatory Geoweb data reporting application, known as SeeClickFix, related to flood events in the context of socioeconomic and biophysical vulnerabilities. Empirical data analysis to determine the socio-spatial characteristics of flood-related reporting trends between 2013 and 2017 from four diverse neighborhoods highlights two important capabilities of crowdsourced data i) identify unique problems experienced by diverse communities and thus address marginalization in managing flood related problems and ii) identify the patterns in types of problems experienced by neighborhoods characterized by divergent socioeconomic and biophysical vulnerabilities. Further, analysis of adoption rates of SeeClickFix in the context of neighborhood characteristics provides valuable insights into the scope of voluntary geographic information (VGI) and its potential role in participatory decision-making. Results indicate that problems of standing water/mosquito complaints are the majority of flood related reports in a high socioeconomic status and biophysically vulnerable neighborhoods, while stormwater infrastructure dominates repeated reports in socioeconomically vulnerable area, suggesting inadequate maintenance of critical infrastructures. Finally, the early adoption of the app and steady increase of reports in poorer neighborhoods during the study period indicates that crowdsourcing of data through a participatory Geoweb has the potential to give voice to marginalized communities and foster transparency.

International audience; This work studies multiscale analyses and characterizations of surface topographies from the engineering and scientific literature with an emphasis on production engineering research and design. It highlights methods that provide strong correlations between topographies and performance or topographies and processes, and methods that can confidently discriminate topographies that were processed or that perform differently. These methods have commonalities in geometric characterizations at certain scales, which are observable with statistics and measurements. It also develops a semantic and theoretical framework and proposes a new system for organizing and designating multiscale analyses. Finally, future possibilities for multiscale analyses are discussed. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.

The classification accuracy of remotely sensed data and its sensitivity to classification algorithms have a critical importance for the geospatial community, as classified images provide the base l...

Summary Contamination of wells with nitrate-N (NO3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh–Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO3-N concentration dataset. After model training, the AI models are verified by the second NO3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the pollution risk.

Seagrass is an important component of coastal marine ecosystems. Seagrass mapping provides a means for assessing seagrass health by monitoring the spatial distribution and density of seagrass habitat in coastal waters. Recent image processing and satellite technologies present the opportunity to leverage quantitative techniques that have the potential to improve upon traditional photo-interpretation techniques in terms of cost, mapping fidelity, and objectivity. Integrated spatial and spectral processing techniques were identified as an alternative method for mapping seagrass extent and density from an IKONOS satellite image of Springs Coast, Florida. These spatially enhanced integrative mapping techniques objectively standardize seagrass-monitoring efforts and enhance mapping capabilities by characterizing spatial seagrass density gradients. A combination of water column correction, pixel classification, and image segmentation techniques provided a seagrass density index map that represented seagrass density and distribution with high spatial detail and overall accuracy 77% comparable to photo-interpretation techniques. Satellite imagery-based spatially enhanced image processing techniques were found to provide a consistent, quantitative, and cost-effective alternative for seagrass mapping in Springs Coast with the potential to be transferred to other parts of the world. A cost savings analysis concluded that there was a 13% cost saving using satellite photo-interpretation and a 47% cost saving using enhanced satellite classification when compared to aerial photo-interpretation.

While the spatial heterogeneity of many aquatic ecosystems is acknowledged, rivers are often mistakenly described as homogenous and well-mixed. The collection and visualization of attributes like water quality is key to our perception and management of these ecosystems. The assumption of homogeneity can lead to the conclusion that data collection from discrete, discontinuous points in space or time provide a comprehensive estimate of condition. To counter this perception, we combined high-density data collection with spatial interpolation techniques to created two-dimensional maps of water quality. Maps of four riverine transitions and habitats – wetland to urban, river to reservoir, river to estuary and a groundwater intrusion – were constructed from the continuous data. The examples provided show that the most basic water quality parameters - temperature, conductivity, salinity, turbidity, and chlorophyll florescence - are heterogeneous at spatial scales smaller than those captured by common point sampling statistical strategies. The 2-dimensional, interpolation-based maps of the Hillsborough River (Tampa, FL) show significant influences of a variety of geographic features including tributary confluences, submarine groundwater inflow, and riparian interfaces. We conclude that many sampling strategies do not account for the type of patchy heterogeneity observed. The integration of existing in-situ sensors, inexpensive autonomous sampling platforms, and geospatial mapping techniques provides high resolution visualization that can adds a more comprehensive geographic perspective needed for environmental monitoring and assessment programs.

This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing e-insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd.

As changes in landuse and the demand for water accelerate, regulators and resource managers are increasingly asked to evaluate water allocation against the need for protection of in-stream habitat. In the United States, only a small number of river basins have the long-term hydrograph data needed to make these assessments. This paper presents an example of how to bridge the conceptual and physical divide between GIS-based watershed modelling of basin-discharge and in-stream hydraulic habitat models. Specifically, we used a Soil and Water Assessment Tool (SWAT) model for the Hillsborough River to produce data for use in a Physical HABitat SIMulation (PHABSIM) model of the same river. This coupling of models allowed us to develop long-term discharge data in ungauged river systems based on watershed characteristics and precipitation records. However this approach is not without important limitations. Results confirm that accuracy of the SWAT-predicted hydrograph declines significantly when either the DEM resolution becomes too coarse or if DEM data are resampled to a coarser or finer resolution. This is due to both changes in the size and shape of the river basin with the varying DEMs and subsequent shifts in the proportions of land use, soils and elevation. Results show the use of 30 m DEMs produced hydrographic patterns amenable for using in-stream habitat protocols like PHABSIM model, especially where little or no hydrographic and land use information exists. Copyright © 2010 John Wiley & Sons, Ltd.

Resolution of data is a sensitive issue in environmental modeling. Professionals from diverse background use Geographic Information Systems (GIS) for environmental modeling. Availability of GIS software and publicly available digital data make modeling more accessible to anyone, but without proper understanding of the resampling and resolution of the digital database, model results are of little practical significance. It is imperative that the government agencies and their contractors responsible for using GIS-based modeling to implement regulatory goals must have strong foundational knowledge of resolution of data and their implications. This study utilizes the SWAT (Soil and Water Assessment Tool) model integrated with ArcView to examine how sensitive the SWAT model was to the resolution of Digital Elevation Models (DEMs) while predicting the streamflow. This research uses a case study to demonstrate to the modeling community that resolution of data matters when predicting flow. If there were no effect of resolution on the modeled results then the original 90 m DEM and the original 30 m DEM resampled to 90 m would show similar trends. Initial input layers into SWAT were: DEMs, soils, landuse (LU) and meteorological data. The model-predicted streamflow was validated against USGS (US Geological Survey) stream gauge data. DEMs are available at 30, 90 and 300 m resolution originally; therefore, this study analysed the sensitivity of the predicted streamflow at 30, 90 and 300 m resolution. Results indicated that SWAT is indeed sensitive to the resolution of the DEMs: original 90 and 30 m DEM resampled to 90 m did not show the same trend. Therefore, the effects of resolution cannot be ignored and resampling may not be adequate in modeling stream flows using a distributed watershed model. Copyright © 2009 John Wiley & Sons, Ltd.

Accurate and inexpensive identification of potentially contaminated wells is critical for water resources protection and management. The objectives of this study are to 1) assess the suitability of approximation tools such as neural networks (NN) and support vector machines (SVM) integrated in a geographic information system (GIS) for identifying contaminated wells and 2) use logistic regression and feature selection methods to identify significant variables for transporting contaminants in and through the soil profile to the groundwater. Fourteen GIS derived soil hydrogeologic and landuse parameters were used as initial inputs in this study. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) were used as an output target class. The use of the logistic regression and feature selection methods reduced the number of input variables to nine. Receiver operating characteristics (ROC) curves were used for evaluation of these approximation tools. Results showed superior performance with the NN as compared to SVM especially on training data while testing results were comparable. Feature selection did not improve accuracy; however, it helped increase the sensitivity or true positive rate (TPR). Thus, a higher TPR was obtainable with fewer variables.

Adequate characterization of potential evapotranspiration (PET) plays a critical role in hydrologic budgets, rainfall–runoff models, infiltration calculations, and drought prediction models (to name a few applications). The availability of reliable and continuous meteorological data remains a challenge; therefore, it is common to use modeled (simulated) meteorological data. This research used the Soil and Water Assessment Tool (SWAT) to estimate PET using different meteorological input data (simulated vs. real data) and the three commonly used PET calculation methods (viz. Penman–Monteith, Hargreaves, and Priestley-Taylor). The overall goal of this research was to determine the accuracy of prediction using simulated and real meteorological data when used with three PET calculation methods. Initial input layers to SWAT were: digital elevation models, soils, and land use. Real meteorological data were obtained from three local meteorological stations, whereas simulated meteorological data were generated by SWAT using one nearby national meteorological site. The model-predicted PET results were validated using independent PET measurements from Florida Automated Weather Network sites. The results of the study indicate that the difference in predicted PET between simulated (modeled) and real meteorology for a given PET calculation method is not significant; however, it is significant across the methods of PET calculation.

Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less.

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.

Contamination of groundwater has become a major concern in recent years. Since testing of water quality of all domestic and irrigation wells within large watersheds is not economically feasible, one frequently used monitoring strategy is to develop contamination potential maps of groundwater, and then prioritize those wells located in the potentially highly contaminated areas for testing of contaminants. However, generation of contamination potential maps based on groundwater sensitivity and vulnerability is not an easy task due inherent uncertainty. Therefore, the overall goal of this research is to improve the methodology for the generation of contamination potential maps by using detailed landuse/pesticide and soil structure information in conjunction with selected parameters from the DRASTIC model. The specific objectives of this study are (i) to incorporate GIS, GPS, remote sensing and the fuzzy rule-based model to generate groundwater sensitivity maps, and (ii) compare the results of our new methodologies with the modified DRASTIC Index (DI) and field water quality data. In this study, three different models were developed (viz. DIfuzz, VIfuzz and VIfuzz_ped) and were compared to the DI. Once the preliminary fuzzy logic-based (DIfuzz) was generated using selected parameters from DI, the methodology was further refined through VIfuzz and VIfuzz_ped models that incorporated landuse/pesticide application and soil structure information, respectively. This study was conducted in Woodruff County of the Mississippi Delta region of Arkansas. Water quality data for 55 wells were used to evaluate the contamination potential maps. The sensitivity map generated by VIfuzz_ped with soil structure showed significantly better coincidence results when compared with the field data.

Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to examine the sensitivity of neuro-fuzzy models used to predict groundwater vulnerability in a spatial context by integrating GIS and neuro-fuzzy techniques. The specific objectives are to assess the sensitivity of neuro-fuzzy models in a spatial domain using GIS by varying (i) shape of the fuzzy sets, (ii) number of fuzzy sets, and (iii) learning and validation parameters (including rule weights). The neuro-fuzzy models were developed using NEFCLASS-J software on a JAVA platform and were loosely integrated with a GIS. Four plausible parameters which are critical in transporting contaminants through the soil profile to the groundwater, included soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the A horizon, and landuse. In order to validate the model predictions, coincidence reports were generated among model inputs, model predictions, and well/spring contamination data for NO3-N. A total of 16 neuro-fuzzy models were developed for selected sub-basins of Illinois River Watershed, AR. The sensitivity analysis showed that neuro-fuzzy models were sensitive to the shape of the fuzzy sets, number of fuzzy sets, nature of the rule weights, and validation techniques used during the learning processes. Compared to bell-shaped and triangular-shaped membership functions, the neuro-fuzzy models with a trapezoidal membership function were the least sensitive to the various permutations and combinations of the learning and validation parameters. Over all, Models 11 and 8 showed relatively higher coincidence with well contamination data than other models. The strength of this method is that it offers a means of dealing with imprecise data, therefore, is a viable option for regional and continental scale environmental modeling where imprecise data prevail. The neuro-fuzzy models, however, should only be used as a tool within a broader framework of GIS, remote sensing and solute transport modeling to assess groundwater vulnerability along with functional, mechanistic and stochastic models.

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.

Contamination of ground water has been a major environmental concern in recent years. The potential for ground-water contamination by pesticides depends on porous media, solute, and hydrologic parameters. Although sophisticated deterministic computer models are available for assessing aquifer-contamination potential on a site-by-site basis, most deterministic models are too complex for vulnerability assessment on a regional scale because they require input data that are spatially and temporally variable, and which may not be available at this scale. Therefore, development of an affordable model that is robust under conditions of uncertainty at the watershed scale with minimum input of field data becomes a useful ground-water management tool. The purpose of this study was to examine the usefulness of fuzzy rule-based techniques in predicting aquifer vulnerability to pesticides at the regional scale. The objectives were to (1) develop fuzzy rule-based models using the same input parameters contained in an i...

Rivers, estuaries, reservoirs, and lakes are multi-use systems that supply water for agricultural, industrial, and human consumption while simultaneously assimilating both point- and non-point source discharges. Existing methods of data collection are generally limited to snapshots in space and time while a comprehensive view of spatial variability remains elusive. Accelerating the integration of existing in-situ sensors, geospatial analysis techniques, and reliable autonomous sampling platform technologies provide immediate improvements for sampling and assessment programs. We provide a demonstration of this integration for high spatial resolution sampling and analysis in a non-wadeable river with an inexpensive unmanned sampling platform (USV), standards sensor arrays, and widely used geospatial techniques. These are used to creating 2-D maps of temperature, conductivity, salinity, turbidity, chlorophyll florescence and chromophoric dissolved organic matter (CDOM). 2-D surface water quality maps show significant influences on local water quality from tributary confluences, submarine groundwater plumes, floodplain/riparian interfaces and other patchily distributed limnological features. Moreover, this project demonstrates how sensors, autonomous vehicles, and geospatial technologies work in concert to create a more comprehensive spatial picture compared to the standard systematic sampling grid with data displayed as means and standard deviations.

Tidal rivers and accompanying coastal environments represent critical links between the open estuary and the local tributary and watershed. In Florida, these coastal ecosystems are multi-use systems. They are often a primary source of water for agricultural, industrial, and human consumption all while functioning as commercial and recreational shipping thoroughfares and receiving storm water runoff and NPDES discharges. In addition to their importance for those direct human uses, their quality and characteristics directly affect the spawning, nursery, and juvenile habitats for numerous commercial and sport fisheries. Thus monitoring and assessment, especially identifying spatial patterns or trends in water chemistry (e.g. temperature, conductivity, salinity, turbidity, chlorophyll, dissolved organic matter and dissolved gasses), of these tidal environments can represent a complex sampling and analysis challenge. There is a perception that sampling and analyzing parameters at regularly spaced intervals over the surface area of a river system will be representative of general trends. However, standard sampling strategy assumes both that parameters will change in a consistent longitudinal and downstream manner and that the average of a parameter is the level where negative impacts occur. Using an innovative combination of unmanned surface vehicles (US V) and geospatial analytical techniques, we will show that this perception is not an entirely accurate or complete view.

The loss of topsoil from the landscape increases sediment loading to adjacent aquatic systems, which may be transported further downstream. Increased sediment loading to downstream ecosystems such as estuaries can negatively impact these environments. This research compares the sediment loss from two watersheds [Jobos Bay (JB) and Rio Espiritu Santo (RES) watersheds] on the island of Puerto Rico. The revised universal soil loss equation (RUSLE) was applied to both watersheds. Results from this study suggested that potential soil loss from both watersheds is relatively low. There is some indication that the RES watershed may have a higher soil loss potential when compared to the JB watershed.

Artificial wetlands are characterized by straight lines and simple perimeters such as circles or squares, whereas natural wetlands show far more complex shapes. Fractal dimension analysis provides a quantitative measure of the curves for the edge of an object. This study uses fractal theory to analyze the characteristic of an object's shape to differentiate natural wetlands from artificial wetlands. The objectives of this study were to: 1) determine how the shape complexity metrics varies between raster and vector formats and 2) if there is a quantifiable difference between patch metrics of the fractal dimension of natural vs. man–made wetlands.

There are many pressures on Florida's water resources. Industrial, agricultural and urban development over the years has impacted water quality adversely. Deterioration of groundwater quality, a major source of fresh water, is a major concern for long–term sustainable growth. The study area, Southwest Florida Water Management District (SWFWMD) of Florida contains one of the nation's fastest growing metropolitan areas. Although landuse changes as a result of population growth is inevitable, it is not too late to try to understand the relationship among landuse change, population growth and environmental dynamics. A thorough understanding of the population growth, landuse change and environmental dynamics is necessary for managing the urban sprawl with minimal environmental impact viz. their impact on water quality and quantity. The objectives of this study were: to explore if there is a spatial relationship exists among NO3 and Bromacil contamination and critical physical/environmental variables and 2) to create a dataset that will be the basis for future study. This was accomplished by studying landuse (1988 and 1999), population (1990s and 2000s), soils, groundwater quality data (1990 and 2000), Floridan Aquifer Vulnerability Assessment (FAVA) and Digital Elevation Models (DEMs). Preliminary results show that contaminated wells were associated with urban and agricultural landuse and sandy soils with high permeability. No significant relationship between population and groundwater quality exists for NO3 and Bromacil contaminated wells.