Applicability of Neuro-fuzzy techniques in predicting ground water vulnerability: A sensitivity analysis.

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

Ground water vulnerability mapping: a GIS and fuzzy rule based integrated tool.

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

A case study using support vector machines, neural networks and logistic regression in a GIS to predict wells contaminated with nitrate-N.

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

Prediction of ground water vulnerability using an integrated GIS-based neuro-fuzzy techniques.

There is a need to develop new modeling techniques that assess ground water vulnerability with less expensive data and which are robust when data are uncertain and incomplete. Incorporation of Geographic Information Systems (GIS) with a modeling approach that is robust has the potential for creating a successful modeling tool. The specific objective of this study was to develop a model using Neuro-fuzzy techniques in a GIS to predict ground water vulnerability. The Neuro-fuzzy model was developed in JAVA using four plausible parameters deemed critical in transporting contaminants in and through the soil profile. These parameters include soil hydrologic group, depth of the soil profile, soil structure pedality points) of the soil A horizon and landuse. The model was validated using nitrate-N concentration data. The majority of the highly vulnerable areas predicted by the model coincided with agricultural landuse, moderately deep to deep soils, soil hydrologic group C (moderately low Ksat) and high pedality points (high water transmitting properties of the soil structure). The proposed methodology has potential for facilitating ground water vulnerability modeling at a regional scale and can be used for other regions, but would require incorporation of appropriate input parameters suitable for the region. This study is the first step toward incorporation of Neurofuzzy techniques, GIS, GPS and remote sensing in the assessment of ground water vulnerability from non-point source contaminants

GIS and geocomputation for water resources science and engineering.

GIS and Geocomputation for Water Resource Science and Engineering not only provides a comprehensive introduction to the fundamentals of geographic information systems but also demonstrates how GIS and mathematical models can be integrated to develop spatial decision support systems to support water resources planning, management and engineering. The book uses a hands-on active learning approach to introduce fundamental concepts and numerous case-studies are provided to reinforce learning and demonstrate practical aspects. The benefits and challenges of using GIS in environmental and water resources fields are clearly tackled in this book, demonstrating how these technologies can be used to harness increasingly available digital data to develop spatially-oriented sustainable solutions. In addition to providing a strong grounding on fundamentals, the book also demonstrates how GIS can be combined with traditional physics-based and statistical models as well as information-theoretic tools like neural networks and fuzzy set theory

A comparison of SWAT model-predicted potential evapotranspiration: Using real and modeled meteorological data.

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

Effects of urbanization on streamflow using SWAT with real and simulated meteorological data.

This research reports the applicability of a hydrological model, the Soil and Water Assessment Tool (SWAT), integrated with a Geographic Information System (GIS) to examine the effects of landuse (LU) change (urbanization) on a streamflow for the Charlie Creek watershed (FL, U.S.A.). This watershed was selected because it is intensely studied by various state and federal agencies and has long-term historical data for meteorology and stream discharge. The overall goal of this project is to examine applicability of the SWAT model to predict streamflow with varying LU and meteorological data including the ability to predict hydrographs for future LU. This goal was accomplished by comparing SWAT-generated hydrographs for i) various LU scenarios (viz. to determine the effects of urbanization on streamflow/hydrograph) with current and future/simulated LU, and ii) real and simulated meteorological data. The resultant ‘SWAT model-predicted streamflow’ for all LU and meteorological scenarios (real and simulated datasets) were then validated with United States Geological Survey (USGS) ‘measured streamflow data’. The SWAT model’s built-in method was used to generate simulated meteorological data. Results indicate that the SWAT model can facilitate analysis of various LU change (for example, as the LU changed to more urban) on the streamflow. However, results also show that the SWAT model-predicted hydrographs were sensitive to the meteorological data used (i.e. real vs. simulated and short-term vs. long-term)

Determination of contaminated wells to NO3-N: A novel vulnerability assessment tool.

Contamination of well with nitrate-N (NO3-N) possess various threats to human health. This problem becomes even more critical when these wells serve as source of drinking water as in the case of many rural parts of USA. This article employs Relevance Vector Machine (RVM) for determination of non-contaminated and contaminated well with nitrate-N (NO3-N) in Polk County, Florida (USA). This research will provide a regional scale integrated GIS-based modeling approach to predict NO3-N contamination of ground water in a cost effective way. This approach also allows for higher true positive results (TPR) with fewer variables when data are imprecise and full of uncertainty which is common with available regional scale data). RVM technique is a Bayesian extension of the Support Vector Machine (SVM). Here, the RVM has been used as a classification tool. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) has been used to develop the RVM model. An equation has been also presented from the developed RVM model. The developed RVM has been compared with the Artificial Neural Network (ANN) and SVM models. This study shows that the developed RVM produces promising result for prediction of non-contaminated and contaminated well with N. The model is important because its real world applications enable water managers to more effectively manage contaminant levels within specific watersheds

Examining spatio-temporal relationships of landuse change, population growth and water quality in the SWFWMD.

There are many pressures on Florida\u27s 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\u27s 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

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoir.

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 ε-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