Search

Browse

• Communities
• Authors
• Year
• Titles
• Subjects

My Space

• Login

Development of space-time geostatistical models for hydrological applications

Theodoridou Panagiota

Full record

---

Summary

Environmental systems are becoming increasingly complex and difficult to predict due to factors such as climate change and limited resources, requiring modeling methods that go beyond those commonly used today. As sustainable management depends on accurate predictions of environmental changes such as groundwater levels, insufficient data insufficiency remains a challenge due to logistical and financial constraints. Despite their usefulness, conventional geostatistical models are in some cases unable to capture the spatial and temporal patterns that determine environmental processes. Conventional geostatistical methods often encounter obstacles when it comes to accurately capturing spatial diversity in hydrological groundwater systems - a key aspect of efficient resource management. A major limitation of these methods is that groundwater properties are not included in the covariance functions; this omission can lead to an incomplete representation of the various factors that influence groundwater behavior. The environmental science community is increasingly recognizing the need to further develop models by integrating physical parameters into covariance functions to better capture the complexity of groundwater systems. This research aims to overcome these obstacles by developing geostatistical methods that improve the precision and confidence in environmental models, even when little data is available. By improving techniques for predicting and interpreting change, this study helps to address the urgent need for more effective tools for resource management decision making.The focus here is on improving the capabilities of models for groundwater management, which is facing exposed to increasing risks due to climate variability. The motivation for this dissertation lies in the increasing need to improve the accuracy of environmental models in situations where spatial and temporal data are limited or missing. This includes systems such as groundwater changes and weather patterns that are linked to temporal and spatial factors. In the field of geostatistics related to groundwater systems, a major challenge is the integration of physical concepts - such as aquifer thickness, hydraulic conductivity and correlation length - into covariance models. These elements play an important role in accurately representing the nuanced spatial and temporal shifts in groundwater systems. This research, presents two novel covariance functions are presented that incorporate these physical principles to address the shortcomings of conventional models. In the field of environmental science and data analysis, currently used methods cannot fully capture the complex interrelationships between different variables if the data are not uniform. Common methods tend to treat spatial and temporal aspects as isolated entities, which can lead to incomplete or incorrect predictions that hinder successful environmental planning efforts. This research aims to address these shortcomings by developing advanced geostatistical models that provide deeper insight into the ever-changing dynamics of the environment. These features are intended to go beyond assumptions about spatial and temporal relationships by integrating the underlying physical mechanisms to improve the accuracy and realism of environmental models.In this work, fuzzy logic is also combined with kriging estimation to determine the extent to which spatial data points are connected in a distance range defined by a fuzzy logic system. Neighborhood selection can thus be modified to account for uncertainties and ambiguities in spatial relationships. The innovative implementation of a fuzzy logic-based kriging method aims to improve the reliability of groundwater predictions in areas with limited data by incorporating these advanced methods. It therefore represents an alternative that dynamically adapts the selection of prediction districts to the degree of affiliation. Its adaptability in the interpolation of data takes into account the uncertainty in environmental systems and significantly increases the accuracy of predictions.In conventional geostatistical methods such as kriging, the distance between the observed data points and the estimated points is calculated using the Euclidean measure. This study focuses on non-Euclidean distance metrics that can more accurately represent the proximity between elements in different environmental systems. This different approach enables a precise representation of distance patterns in complex spatial environments that are inherently irregular and diverse. In addition, the study evaluates criteria for fitting variograms to increase the accuracy of model selection and achieve a better representation of spatial dependence and prediction results.The techniques presented in this study provide information on groundwater levels and offer important perspectives for future water management and climate adaptation plans. In this way, this work aims to improve the understanding of groundwater behavior and thus facilitate informed and sustainable decisions. The new ideas proposed here are likely to impact the field of geostatistics and environmental modeling by providing scientifically sound and practical solutions. The effective combination of geostatistical techniques and fuzzy logic systems in this study opens up opportunities for building more accurate and reliable environmental models that could impact sustainable resource management in various industries.

Available Files

Services