Geospatial Time Series Analysis for Coastal Systems: AI-Powered NARX Neural Networks Integrating Remote Sensing for Advanced Shoreline Change Prediction

In the background of ongoing climate change, it is important to monitor the spatial and temporal changes of glacial lakes (GLs) since they influence snowmelt runoff, stream discharge, water resources, and glacial lake outburst flood (GLOF). However, accurate identification and mapping of GLs in the background of snow-clad mountains through visual interpretation of satellite data is a tedious and challenging assignment when multiyear time-series analysis is considered. To overcome this challenge, automated extraction of GLs in satellite images has been carried out in this study with the help of machine learning (ML). The novelty of this study is identification and tracking of GLs over three decades using ML and geospatial analysis using pixel-based image classification. For this, Random Forest Classifier (RFC) and Artificial Neural Network (ANN) were employed. The methodology is demonstrated here for the identification and mapping of GLs in the Sikkim Himalaya from 1987 to 2020 and for forecasting the possible fate of these GLs through time-series modelling. The geospatial time-series analysis using Google Earth Engine, ML classifiers, and GIS framework, has captured the dynamics of GLs in Sikkim and has revealed the spatial and temporal patterns in GLs’ dimensions as well as GLOF risk.

<p>Frost is one of the most damaging hazards in agriculture as its impacts negatively cropland yield and agro-ecosystems, affecting price commodities of agricultural products. Locating the spatiotemporal patterns of frost events can be a challenging and costly task since a dense network of weather stations is required to accurately characterize frost distribution. The recent advancements in geoinformation technology have enhanced our ability to retrieve parameters that are critical to the development of frost conditions such as land surface temperature (LST). In addition, the availability of cloud-based imagery processing platforms allows to easily acquire and process LST data over large scales setting the EO field as the optimal mean for frost risk mapping. The present study imprints the frost’s spatial patterns analyzing geospatially referenced frost incident field-based data conducted by the Greek National of Agricultural Payments Agency (ELGA) during the period 2015-2020. In addition, a cloud-based methodological framework is introduced herein based on a time series analysis with LST data from ESA's Sentinel-3 LST operational product to map frost occurrence. The proposed approach was implemented for the same time period as that of the ELGA data. The frost frequency maps developed by the two approaches were  compared using appropriate geospatial data analysis methods in order to determine their correspondence. Results generally demonstrated the added value of EO data in identifying the frost risk degree and geographical extent for all the years analysed. Our proposed methodology has a promising potential to be used at operational scale to map frost risk conditions and to also support decision making in frost hazard mitigation.</p><p><strong>KEYWORDS: </strong><em> cloud-based platform, LST,  Sentinel 3, frost risk, geospatial analysis </em></p>

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.

A comprehensive hydrogeochemical and geospatial analysis has been carried out for the assessment of geochemical evolution and groundwater potability of the coastal aquifer system underlying the Mahakalapara Block, Odisha, India. The major ion chemistry of groundwater is primarily made up of alkali cations and strong acidic anions having a strong influence of the aquifer lithology. Geochemical analysis of the sample points towards the occurrence of dissolution and ion exchange processes across the study area. Spatially the former is the principal influencing process along the western part of the block whereas the latter is active across the entire study area. Increased alkali and chloride concentration during post-monsoon period indicates potential saline and formational water influences. Potability analysis of the samples is suggestive of widespread unsuitability for domestic, agricultural and industrial uses. All the evaluating parameters except pH and residual sodium carbonate indicate a general unsafe characteristic of the subsurface water for domestic and agricultural utility. From an industrial perspective, the subsurface waters are corrosive but not incrusting.

Geospatial analysis, driven by the evolution of Geographic Information Systems (GIS) and remote sensing technologies, plays an increasingly pivotal role in urban planning. This study examines the historical, technological, and methodological foundations of geospatial analysis and its growing significance in shaping contemporary urban environments. Beginning with a historical overview of urban planning, the paper discusses the transition from classical design to data-driven planning. It examines the expanding typologies of geospatial data, including vector and raster models, volunteered geographic information, and participatory platforms that have transformed how spatial knowledge is produced and applied. The integration of spatial analysis methods, including spatial clustering and autocorrelation techniques, enhances the ability of planners to assess urban growth, traffic safety, land use, and environmental impact with greater precision. Remote sensing advancements, alongside machine learning techniques, further support urban land use monitoring. Despite the promise, challenges such as data accessibility, quality verification, equitable usage, and effective knowledge visualization persist. This paper argues that while the tools of geospatial analysis are powerful, achieving sustainable, inclusive urban futures will depend on resolving issues of cost, accessibility, and interpretability for diverse stakeholders. Keywords: Geospatial Analysis, Urban Planning, Geographic Information Systems (GIS), Remote Sensing, Spatial Data, Urban Development, Participatory GIS

Background of the Study: The study area is located within four communities in Akuku-Toru Local Government Area, which is a coastal region within the Niger Delta. The study area is heavily reliant on groundwater for domestic, industrial, and agricultural purposes. The hydrogeological dynamics of the area are complex, with diverse geological formations and intricate subsurface structures. As a result, an innovative and integrated approach is necessary for effective groundwater management. The study investigated the potential of groundwater resources in the study area and identification of fresh water zones using electrical resistivity, remote sensing, and GIS which employs geophysical surveys, remote sensing techniques, and geospatial analysis to explore the interplay between aquifer characteristics, geological formations, and topographical attributes. The fresh water zones are regions with low saline content. Aim: This study aims to assess groundwater potential in some parts of Akuku-Toru Local Government Area by integrating the geophysical data from Vertical Electrical Sounding (VES) surveys with geospatial analysis from GIS and Remote Sensing Technology. The research seeks to provide a comprehensive understanding of groundwater availability and its correlation with geophysical and geospatial parameters. Study Design: A thorough methodology was employed to investigate the possibility of freshwater resources in the study area. The approach involved gathering Vertical Electrical Sounding (VES) data from 8 locations, as well as incorporating geospatial data such as elevation, drainage density, geology, apparent resistivity, and slope maps. The collected data underwent rigorous processing, correlation analysis, and reclassification to explore the potential of freshwater resources in the study area. Place and Duration of the Study: The research was conducted in four communities (Abonnema, Ekulama, Jacobkiri and Belema) within the Akuku-Toru Local Government Area over a span of 18months. The area's hydrogeological context and topographical features are investigated to determine groundwater potential zones. Methods: The research utilized the Vertical Electrical Sounding (VES) method to obtain aquifer resistivity data, reflecting subsurface Lithological variations. Geospatial analysis involved accessing elevation and drainage density patterns. Correlation analysis was also performed to link the geophysical and geospatial data with qualitative interpretations, facilitating the assignment of numerical values representing groundwater potential zones. Results: The Correlation analysis revealed insightful patterns. Aquifer resistivity, elevation and slope were identified as influential parameter affecting groundwater potential. The geology of the study area, categorized into dominant formations, exhibited varying degrees of potential for freshwater resources. The Correlation of geophysical and geospatial data provided a comprehensive understanding of groundwater availability across the study region. Conclusion: The integration of geophysical and geospatial analysis offers a robust approach to groundwater potential assessment. The research findings contributed to valuable insights into the spatial distribution of potential freshwater resources in the study area. The correlation between aquifer resistivity, elevation, slope, and geology enhanced our understanding of hydrological conditions and provides a foundation for future groundwater studies and management strategies.

Our ability to predict sandy shoreline evolution resulting from future changes in regional wave climates is critical for the sustainable management of coastlines worldwide. To this end, the present generation of simple and efficient semi-empirical shoreline change models have shown good skill at predicting shoreline changes from seasons up to several years at a number of diverse sites around the world. However, a key limitation of these existing approaches is that they rely on time-invariant model parameters, and assume that beaches will evolve within constrained envelopes of variability based on past observations. This raises an interesting challenge because the expected future variability in key meteocean and hydrodynamic drivers of shoreline change are likely to violate this ‘stationary’ approach to longer-term shoreline change prediction. Using a newly available, multi-decadal (28-year) dataset of satellite-derived shorelines at the Gold Coast, Australia, this contribution presents the first attempt to improve multi-decadal shoreline change predictions by allowing the magnitude of the shoreline model parameters to vary in time. A data assimilation technique (Ensemble Kalman Filter, EnKF) embedded within the well-established ShoreFor shoreline change model is first applied to a 14-year training period of approximately fortnightly shoreline observations, to explore temporal variability in model parameters. Then, the magnitudes of these observed non-stationary parameters are modelled as a function of selected wave climate covariates, representing the underlying seasonal to interannual variability in wave forcing. These modelled time-varying parameters are then incorporated into the shoreline change model and tested over the complete 28-year dataset. This new inclusion of non-stationary model parameters that are directly modelled as a function of the underlying wave forcing and corresponding time scales of beach response, is shown to outperform the multi-decadal predictions obtained by applying the conventional stationary approach (RMSEnon-stationary = 11.1 m; RMSEstationary = 254.3 m). Based on these results, it is proposed that a non-stationary approach to shoreline change modelling can reduce the uncertainty associated with the misspecification of physical processes driving shoreline change and should be considered for future shoreline change predictions.

This study was performed along the shorelines of Lake Salda in Turkey during the elapsed period from 1975 to 2019 in order to detect shoreline changes. Within this framework, geographic information system, digital shoreline analysis system, Modified Normalized Difference Water Index, and multi-temporal satellite images were utilized. The measurement of shoreline displacement was mainly divided into six analysis regions. In digital shoreline analysis system, several statistical parameters such as end point rate, linear regression rate, shoreline change envelope, and net shoreline movement were computed to measure the rates of shoreline displacement in terms of erosion and accretion. The maximum shoreline change between 1975 and 2019 was determined as 556.45 m by shoreline change envelope parameter. The maximum shoreline change was 16.35 m/year by end point rate parameter and 12.91 m/year by linear regression rate parameter. While erosion has been observed in 3rd, 4th and 6th segments, accretion has been observed in other segments. When all the transects were taken into consideration, an accretion observed. The results indicate that there is a decrease in area of the lake. Experiment results show that integrated use of multi-temporal satellite images and statistical parameters are very effective and useful for shoreline change analysis. It is thought that the structures such as irrigation pond and dam that are built on the streams that recharge the lake and average rainfall and average temperature conditions are the main reasons of the fluctuations and changes in the shorelines.

Study of morphological variations and the effects of oceanographic processes such as erosion and accretion at different temporal scales are important to understand the nature of the coast and the cyclic changes occurring during different seasons. The Udupi-Dakshina Kannada coast along the west coast of India exhibits a wide range of changes depending on the interactions of tide and wave energy, sediment supply and more importantly human intervention. In view of this, the present work has been carried out to study the changes in shoreline changes along the Udupi-Dakshina Kannada coast over a period of 29 years from 1990 to 2019. Remote Sensing and GIS techniques have been used to demarcate shorelines and calculate the shoreline change rates. Overall accretion and erosion rates were found to be 1.28 m/year and 0.91 m/year respectively along the coast. Highest accretion and erosion rates of 12.57 m/year and 5.34 m/year was noticed along the Dakshina Kannada coast. The study also suggests that multi-dated satellite data along with statistical techniques can be effectively used for prediction of shoreline changes.
 Keywords: remote sensing, GIS, Dakshina Kannada coast, oceanography, shoreline.

In recent years, artificial intelligence (AI) and machine learning (ML) have changed geospatial analysis, allowing for more accurate, efficient, and scalable processing of massive volumes of geographical data. Traditionally, geospatial analysis depended on human-driven approaches and rule-based systems, which were frequently time-consuming and restricted in their capacity to handle large datasets. The combination of AI and ML has resulted in the development of revolutionary approaches like as deep learning, neural networks, and automated feature extraction, which have transformed the use of geographic information systems (GIS). This chapter investigates the critical role of artificial intelligence and machine learning in developing geospatial analysis in a variety of disciplines, including environmental monitoring, urban planning, disaster management, and agriculture. AI-powered models can now do predictive analytics, real-time data processing, and pattern identification in satellite images, LiDAR, and sensor networks.

Abstract—This research presents an integrated framework for satellite image retrieval, segmentation, and visualization, utilizing Python-based algorithms and geospatial data processing methods. It incorporates Google Maps-based image acquisition, a Tkinter-powered interactive GUI, and the U-Net segmentation model to efficiently process satellite imagery. Satellite image segmentation, powered by U-Net, plays a vital role in geospatial analysis, enabling automated classification and extraction of key features from high-resolution imagery. The framework facilitates seamless high-resolution map generation, segmentation, and an- alytical visualization through interactive tools like bar graphs and pie charts. By leveraging U-Net’s robust architecture, this implementation enhances segmentation accuracy and supports applications in urban planning, environmental monitoring, and disaster management. Through the automation of retrieval, processing, and visualization of satellite imagery, this research advances geospatial intelligence and provides a scalable solution for efficient image segmentation workflows. Keywords—Satellite Image Segmentation, Land Cover Classi- fication, Remote Sensing, Image Processing, Semantic Segmen- tation, Deep Learning Models, Convolutional Neural Networks (CNNs), U-Net Architecture, Geospatial Analysis, Land Use Analysis

Graphical Abstract Highlight Research DSAS based on geographic information systems has the ability to extract important information on the dynamics of shoreline changes, both accretion and abrasion. The dynamics of shoreline change in the short period of time 2018-2023 in the IWIP industrial area shows a very dynamic change process dominated by accretion. The conversion of beach into land was more prevalent than the process of shoreline retreat in the study area. The abrasion rate in this study area was categorized as moderate, while the accretion rate was categorized as very high due to construction activities. Abstract The development of industrial estate infrastructure in coastal areas causes significant changes in coastal morphology. Despite extensive infrastructure development in coastal zones, limited empirical data exists on the shoreline dynamics of newly established industrial estates, particularly in Eastern Indonesia, thus highlighting the urgency of this study. This study investigates coastal morphology changes in the PT Indonesia Weda Bay Industrial Park (IWIP) industrial area over five years using Landsat 8 OLI level 2A satellite imagery and geospatial analysis. Shoreline extraction was performed using the Normalized Difference Water Index (NDWI) algorithm and analyzed with the Digital Shoreline Analysis System (DSAS) applying the Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR) methods. The findings show that from 2018 to 2023, the shoreline in the PT IWIP area predominantly experienced accretion. The highest rate of shoreline accretion occurred in industrial zone, with a maximum of 147.58 m/year and an average of 36.56 m/year, while residential zones in the eastern and western regions experienced moderate abrasion, with a maximum of 12.32 m/year and an average of 4.11 m/year. Categorization followed standard DSAS criteria, where shoreline changes between 10–30 m/year were considered moderate, and changes above 30 m/year were classified as very high. Measurement accuracy was validated using high-resolution Google Earth imagery and Landsat metadata, ensuring positional accuracy within ±30 meters. These results highlight the rapid and spatially varied shoreline changes driven by industrial activities, emphasizing the importance of remote sensing in monitoring and managing coastal development impacts

In this study, the remotely sensed thermal data from the earth’s surface of the center zone of the earthquake was used to predict the time of earthquake occurrence. The provided methods for approximating the time and severity of the earthquake are classified into two categories of non-smart and smart. In non-smart methods, the earthquake predicting parameters must be obtained continuously and point to point, to prevent the errors caused by interpolation uncertainty, which requires high levels of cost and furthermore we will be limited in terms of tools in these methods. The smart methods which include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Genetic Algorithm (GA), etc., contain different uncertainties depending on their training algorithms in such a way that by defining an inappropriate threshold between the predicted value and the real ones, they are not able to isolate the variable but natural behavior of the under-study area from anomaly. Because a series of time-dependent data should be used in studying earthquakes, the prediction of theses time series can be done using Artificial Neural Networks. To make more accurate, two different methods of dynamic NARX (Nonlinear Auto Regressive with eXternal input) neural network algorithm namely Levenberg-Marquardt and Scaled conjugated gradient has been applied. The responses of these two methods have been compared with the response derived from mean and variance. The important advantage of the NARX neural network is that small thermal anomaly due to the natural climate changes did not detect and considered as earthquake pre-indicator. The results elucidate that the earthquake, 7 days before (first method response) and 12 days before (second method response) occur has been predicted. The thermal anomaly about 5 degrees of kelvin of earthquake center zone detected. Thus the thermal anomaly detected by this method can be a good pre-indicator for earthquake prediction.

This study explores the sustainable development of rural tourism in post-conflict areas, focusing on Ukraine’s Transcarpathia region from 2022 to 2024. The research addresses the critical role of rural tourism in economic recovery, social stability, and cultural preservation in regions affected by conflict, where traditional data collection methods are often disrupted. Utilizing remote sensing and geospatial analysis, the study develops a comprehensive monitoring methodology integrating indicators such as land use changes (LUCI), infrastructure recovery (RSTI), ecological stability (NDVI, NDWI), and socio-economic resilience (DAI, SSELI). The methodology combines satellite data (Sentinel-2, VIIRS) with GIS tools and socio-economic statistics to calculate an integrated index (IRSDRT) for assessing sustainability. Key findings reveal a significant recovery in tourism infrastructure, with RSTI peaking at 120.0 in 2023, and a steady increase in ecological indicators, such as NDVI rising from 28.57 to 36.00. Socially, digital accessibility (DAI) improved from 70% to 88%, supporting tourism growth despite ongoing challenges. The study concludes that geospatial technologies effectively monitor post-conflict recovery, highlighting the resilience of rural tourism in Transcarpathia. The results underscore the importance of balanced development, combining infrastructure, ecological preservation, and digitalization, and provide actionable insights for policymakers to enhance sustainable tourism in similar regions.

The current study intended to geospatially analyze the potentiality and site suitability of geo-ecotourism in West Bengal, India. The state of West Bengal is a great platform for diverse tourism and has enormous potential to cultivate geo-ecotourism, as has come up in recent years. The current effort throws some valuable light on the possibility of turning the many geologically, geomorphologically and ecologically significant tourist spots of West Bengal into geo-ecotourism sites, aided with geospatial techniques. The study deals with the qualitative and quantitative investigation of the potentiality of the whole state by dividing it into several geo-ecotourism zones, based on its physiographic setting and Land Use Land Cover (LULC) features, using satellite image data. The application of geospatial technology combined with Remote Sensing (RS) and Geographic Information System (GIS) was employed for this geospatial analysis to portray the potential zones using cartographic and statistical techniques. Furthermore, nine criteria were selected to run the Analytic Hierarchy Process (AHP) method to determine the site suitability for geo-ecotourism. The present submission attempts to record the mapping and analysis of geo-ecotourism of West Bengal employing a secondary database, an expert’s opinions and primary observations, with the application of the AHP method and GIS. The outcomes of the study were found to be very significant, as they indicate a proviso for geo-ecotourism development in the state and will contribute to the formation of location-specific planning and the sustainable management of geo-ecotourism.