This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing crucial data collection opportunities and increasing exposure to hazards. To address these limitations, we introduce WAITR (Weighted Aggregate Inter-Temporal Reward), a novel path-planning framework that integrates a knowledge graph with pathlet-based planning, segmenting the environment into dynamic, speed-adjusted sub-regions (pathlets). This structure enables coordinated, adaptive planning, as agents can operate within time-bound regions while dynamically responding to environmental changes. WAITR’s cumulative scoring mechanism balances immediate data collection with long-term optimization of Points of Interest (POIs), ensuring safer navigation and comprehensive data coverage. Experimental results show that WAITR substantially improves POI coverage and reduces exposure to hazards, achieving up to 27.1% greater event coverage than traditional greedy methods.

Spatiotemporal networks’ observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network’s initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage.

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.

Marine mammal vocalizations provide a reliable method of identifying most species. These vocalizations may be effectively captured by the passive acoustic monitoring of underwater acoustic signals, but this approach generates prohibitively large amounts of data, making a machine learning approach desirable. In this study, we demonstrate that residual learning networks may be highly effective at classifying vocalizations by species and investigate the utility of multi-channel spectrograms in training these networks. We find that classification performance can be improved by effectively preprocessing the acoustic data. A series of tests are used to assess optimal parameters for noise-removal, spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation. While we find that our networks do learn additional information from multi-channel spectrograms, we demonstrate that single-channel spectrograms still offer superior classification performance in 32-class classification tasks.

We present a novel approach for the prediction of Rogue Waves in oceans using Machine Learning methods. Since the ocean is composed of many wave systems, the change from a bimodal or multimodal directional distribution to unimodal one is taken as the warning criteria. Further, we explore various features that help in predicting Rogue Waves. The analysis of the results shows that the Spectral features are significant in predicting Rogue Waves. Finally, we propose a Random Forest Classifier based algorithm to predict Rogue Waves in oceanic conditions. For a range of windows, the proposed algorithm has accuracies between 89.57% and 91.81%, and the balanced accuracies between 79.41% and 89.03%. Moreover, we have also introduced the publicly available buoy dataset, which can serve as a benchmark dataset.

Understanding and predicting weather behavior is vital for informing pilots about changing flight conditions. This paper presents a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare two datasets for prediction suitability, and a collinearity analysis between these datasets reveals a better model performance with smaller test error with one of them. We then apply machine learning and a genetic algorithm to process this data further and arrive at a competitive error rate. Finally, we create an offline software for wind prediction using the best performing classifier.

A system allowing for the efficient processing and viewing of dense METOC data sets stored in Network Common Data Format (netted) files is developed using advanced bitmap indexing. A method for netted data extraction and bitmap index creation is presented. Efficient geospatial range and pseudo-KNN queries are implemented. A two step filtering algorithm is shown to greatly enhance the speed of these geospatial queries, allowing for extremely efficient processing of the netted data.

Aggregate K Nearest Neighbor (AKNN) queries are problematic when performed within spatial networks. While simpler network queries may be solved by a single network traversal search, the AKNN requires a large number costly network distance computations to completely compute results. The M-Tree index, when used with Road Network Embedding, provides an efficient alternative which can return estimates of the AKNN results. The M-Tree index can then be used as a filter for AKNN results by quickly computing a superset of the query results. The final AKNN query results can be computed by sorting the results from the M-Tree. In comparison to Incremental Euclidean Restriction (IER), the M-Tree reduces the overall query processing time and the total number of necessary network distance computations required to complete a query. In addition, the M-Tree filtering method is tunable to allow increasing performance at the expense of accuracy, making it suitable for a wide variety of applications.

Spatial networks, such as road systems, operate differently from normal geospatial systems because objects are constrained to locations on the network. Performing queries on spatial networks demands entirely different solutions. Most spatial queries make use of an R-Tree to process them efficiently. The M-Tree is a data tree index which is capable of indexing data in any metric space. The M-Tree index can replace the R-Tree index for spatial network queries, such as range and KNN queries. The difficulty is that the M-Tree is only as efficient as the distance algorithm used on the underlying objects. Most network distance algorithms, such as A*, are too slow to allow the M-Tree to operate efficiently on spatial networks. The truncated road network embedding (tRNE) maps the network into a higher dimensional space where any LP metric can be used to efficiently compute an accurate approximation of network distance. The M-Tree combined with tRNE creates an efficient index structure for computing spatial network queries. The M-Tree substantially outperforms network expansion, the most popular method of computing spatial network queries, when performing spatial network KNN and range queries.