Academic literature on the topic 'Artificial Intelligence Machine Learning Algorithms Acoustic Analysis Remote Sensing'

Artificial intelligence (AI) is transforming precision agriculture by enhancing soil analysis, crop tracking, and fertilizer management. AI-powered systems enable real-time monitoring of crop health, soil nutrient levels, and environmental conditions, providing farmers with actionable insights. Machine learning algorithms integrated with remote sensing technologies, such as drones and satellites, help monitor crops, predict yields, and detect early stress signs. In soil research, AI combines data from IoT sensors, GIS, and soil samples to create detailed soil health maps and predict changes. Predictive analytics optimize nutrient management by recommending precise application of water, fertilizers, and amendments based on real-time crop and soil needs. Automation, decision support systems (DSS), and variable rate technology improve input efficiency, reducing waste and environmental impact. By leveraging AI in these areas, precision agriculture promotes sustainable farming, lowers costs, and increases productivity through improved data analysis, real-time monitoring, and automation.

Environmental monitoring and management are critical for sustainable development and the preservation of natural resources. Recent advancements in artificial intelligence (AI) integrated with geospatial technologies promise to revolutionize these fields. Delving into potential breakthroughs, AI offers precise, real-time monitoring of environmental parameters through machine learning (ML) algorithms, remote sensing data, and geographic information systems (GIS). Enhanced data analysis techniques facilitate the early detection of environmental anomalies, predictive modeling of ecological trends, and efficient resource management. Successful implementations of AI in tracking climate change impacts, managing natural disasters, and monitoring biodiversity are presented through various case studies. Challenges such as data privacy, algorithm transparency, and the need for interdisciplinary collaboration are also addressed. Future research directions explore AI's potential to foster more resilient and adaptive environmental management practices. Synthesizing AI and geospatial technology underscore a transformative approach to safeguarding our environment.

"Forest fires in the Mediterranean basin are a major threat to the sustainability of natural ecosystems and the protection of biodiversity. Forest fires affect not only the vegetation, but also the fauna and micro-organisms living in the ecosystem. Fires leading to the extinction of rare and endemic species accelerate the loss of biodiversity and disrupt the balance of ecosystems. This situation creates the need to effectively monitor and analyze the destruction caused by forest fires. Remote sensing technologies and satellite imagery play a critical role in assessing the severity and impact of forest fires. While pixel-based analysis evaluates the spectral characteristics of each pixel, object-based analysis considers homogeneous groups of regions in a broader context. In this study, these two remote sensing approaches were applied to Sentinel-2 satellite imagery for the Muğla fires of 12 and 14 July 2023. Using the pre- and post-fire imagery, burned area and severity analyses were performed using the latest ensemble-based machine learning algorithms: random forest, XGBoost, GBM and NGBoost. For pixel-based classification, the highest accuracy was obtained with the XGBoost algorithm with 90.95% On the other hand, the highest accuracy in object-based classification was calculated with the GBM algorithm with 85.94% The fire intensity analysis was modeled using the USGS FIREMON and the Mediterranean-based Differential Normalized Fire Intensity (dNBR) index values. The SHAP approach, one of the global explainable artificial intelligence approaches, was used to understand the decisionmaking mechanisms of the trained machine learning models and to determine the effectiveness of each feature/index within the model. According to SHAP results, spectral indices of BAI and NDVI in pixel-based classification and RVI in object-based classification were determined as the most effective factors in model predictions. The study highlights the importance of innovative methods for monitoring and managing forest fires, and points to the need for new strategies to conserve biodiversity."

Abstract Ensuring safety remains the paramount concern within railway systems. While extensive research has been conducted on multiple facets of train safety, the ongoing challenge lies in the real-time monitoring and timely detection of defects, including their occurrence, causes, and severity. Optical fiber cable has been proven to sense long-distance condition monitoring by using optical time domain reflectometry (OTDR). Distributed Acoustic Sensing (DAS) uses fiber optic cables along the track to detect any anomaly indicator such as vibration-based defective features. DAS systems can colle

There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt