Bathymetric mapping is traditionally implemented using shipborne single-beam, multi-beam, and side-scan sonar sensors. Procuring bathymetric data near coastlines using shipborne sensors is difficult, however, this type of data is important for maritime safety, marine territory management, climate change monitoring, and disaster preparedness. In recent years, the bathymetric light detection and ranging (LiDAR) technique has been tried to get seamless geospatial data from land to submarine topography. This paper evaluated the accuracy of bathymetry generated near coastlines from satellite altimetry-derived gravity anomalies and multi-beam bathymetry using a tuning density contrast of 5000 kg/m³ determined by the gravity-geologic method. Comparing with the predicted bathymetry of using only multi-beam depth data, 78% root mean square error from both multi-beam and airborne bathymetric LiDAR was improved in shallow waters of nearshore coastlines of the western Korea. As a result, the satellite-derived bathymetry estimated from the multi-beam and the airborne bathymetric LiDAR was enhanced to the accuracy of about 0.2 m.

The accurate measurement of diverse displacements of structures is an important index for the evaluation of a structure’s safety. In this study, a comparative analysis was conducted to determine the integrated RTK-GPS/accelerometer method that can provide the most precise structure displacement measurements. For this purpose, three methods of calculating the dynamic displacements from the acceleration data were comparatively analyzed. In addition, two methods of determining dynamic, static, and quasi-static displacements by integrating the displacements measured from the RTK-GPS system and the accelerometer were also comparatively analyzed. To ensure precise comparison results, a cantilever beam was manufactured onto which diverse types of displacements were generated to evaluate the measurement accuracy by method. Linear variable differential transformer (LVDT) measurements were used as references for the evaluation to ensure accuracy. The study results showed that the most suitable method of measuring the dynamic displacement with the accelerometer was to calculate the displacement by filtering and double-integrating the acceleration data using the FIR band-pass filter. The integration method that uses frequency-based displacement extraction was most appropriate for the integrated RTK-GPS/accelerometer method of comprehensively measuring the dynamic, static, and quasi-static displacements.

This study developed a smartphone application that provides wireless communication, NRTIP client, and RTK processing features, and which can simplify the Network RTK-GPS system while reducing the required cost. A determination method for an error model in Network RTK measurements was proposed, considering both random and autocorrelation errors, to accurately calculate the coordinates measured by the application using state estimation filters. The performance evaluation of the developed application showed that it could perform high-precision real-time positioning, within several centimeters of error range at a frequency of 20 Hz. A Kalman Filter was applied to the coordinates measured from the application, to evaluate the appropriateness of the determination method for an error model, as proposed in this study. The results were more accurate, compared with those of the existing error model, which only considered the random error.

This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH₃-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO₃-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R² was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R² 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water.

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support.

Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.