The simultaneous occurrence of fire-related smoke and combustible gas leakage, particularly methane, poses a significant and complex threat in various environments, including residential kitchens, commercial catering establishments, and industrial workshops. Conventional detection systems are typically designed to monitor a single hazard type, requiring multiple sensors for comprehensive risk coverage. This research aimed to develop a novel, highly integrated, and cost-effective composite detector that can simultaneously monitor smoke and methane.

The foundation of our proposed method is a composite detection model utilizing the optical properties of a single 1653.7 nm laser. This wavelength was specifically chosen because it aligns with a characteristic absorption line for methane and is capable of inducing Mie scattering from smoke particles. The main methodological challenge was to decouple the composite signals, as methane absorption and smoke extinction attenuate the transmitted laser intensity. To achieve this, a sophisticated signal processing strategy was developed. We employed tunable diode laser absorption spectroscopy in a time-division multiplexing scheme using a single triangular wave scan. The rising edge of the scan incorporated high-frequency harmonic modulation, enabling wavelength-modulation spectroscopy (WMS) to extract the second-harmonic signal. By contrast, the falling edge of the scan remained unmodulated, allowing for direct absorption spectroscopy (DAS) to provide robust measurements for higher methane concentrations. The decoupling of the smoke signal was achieved by analyzing the baseline of the scattered light signal, which corresponds to wavelengths where methane absorption is negligible, making its intensity directly proportional to smoke particle concentration. In addition, to mitigate the effects of smoke on methane measurements, all absorption signals were normalized during processing. To further enhance the detector's performance, a novel optical structure was designed to address the inherent weaknesses of the 1653 nm laser, specifically, the relatively weak scattering signal from smoke particles and the need for a long optical path for sensitive methane detection. We implemented a multimirror, "cross-beam" optical path within a compact chamber, which effectively folds the laser beam, causing it to traverse the detection volume multiple times before reaching the photodetector. This innovative configuration resulted in a sixfold extension of the effective absorption path length for methane, increasing it from the standard 10 cm to 60 cm. By optimizing the placement of the mirrors and the angle of the scattering detector, we configured the system to collect scattered light from high-intensity beam-crossing regions, leading to a fourfold enhancement of the collected smoke scattering signal.

In single-analyte tests, the detector exhibited high sensitivity, achieving a lower detection limit of 0.008% volume fraction for methane. The hybrid WMS-DAS approach proved effective, with WMS yielding more accurate results for concentrations below 0.25%. A linear regression analysis of the measurement data indicated excellent linearity (R²=0.9833). For smoke detection, the device achieved a detection limit of 0.05 dB·m^-1. The critical validation was the composite detection experiment, in which methane detection was performed under various stable background smoke densities (0, 0.1, 0.5, and 1.0 dB·m^-1). The results conclusively demonstrated the effectiveness of the signal decoupling methodology, showing that the presence of smoke, even at high densities, did not significantly affect the accuracy of methane concentration measurements. However, at the highest smoke densities (0.5 and 1.0 dB·m^-1), the WMS signal exhibited increased fluctuations, indicating that extreme smoke levels can degrade the signal-to-noise ratio, setting a performance boundary for the current system.

This study proposed and validated a novel method for the synchronous detection of smoke and methane using a single 1653 nm laser source. A functional prototype of the composite detector demonstrated the feasibility and effectiveness of the approach. Major innovations include a composite detection model that enables signal decoupling through a time-division WMS/DAS scheme and a performance-enhancing cross-beam optical path that extends the absorption length sixfold and enhances the scattering signal fourfold. Most importantly, it demonstrated robust performance in composite scenarios with minimal cross-interference. By overcoming the limitations of conventional systems, this single-source composite detection method offers a promising, low-cost, and highly integrated solution, providing a new technical pathway for advanced early warning systems in complex, multihazard environments.

Slope instability early warning systems are used to monitor landslide deformation to ensure proper stakeholders make timely safety decisions and take emergency actions. Acoustic emission signals are constantly produced during landslide deformation and can be employed to monitor slope stability. Acoustic emission technology using an active waveguide has gradually become an effective monitoring method for subsurface deformation of soil landslides. It has the characteristics of low cost and high sensitivity for early detection of minor deformation within slopes. Therefore, acoustic emission technology is anticipated to increase the success rate of landslide risk early warning.

Based on large-scale landslide model experiments and field monitoring studies, the interpretation methods for acoustic emission monitoring data evolved from qualitative to quantitative. Many landslide on-site tests using acoustic emission monitoring revealed a proportional correlation between acoustic emission rate and landslide velocity. This paper described an empirical formula method for quantifying landslide subsurface deformation behavior using acoustic emission data, extracting landslide movement information, and examining the change pattern by inversely calculating landslide displacement, velocity, and acceleration. The threshold of acoustic emission parameters that trigger landslide warnings could be obtained based on the landslide velocity classification standard. However, challenges remained in developing widely applicable methods for quantifying acoustic emission data, such as the diversity of conditions in the monitoring equipment and the complexity of the interaction within the active waveguide. These challenges limited the accurate quantification of the deformation-acoustic emission response relationship, and thus, the reliability of landslide warning results could not be ensured. To overcome the above limitations, a machine learning approach was proposed, which could automatically interpret acoustic emission monitoring data and quantify the response relationship between deformation and acoustic emission. An automatic classification model for the landslide motion state and a prediction model for landslide displacement were developed to accurately measure the representative deformation characteristics, including landslide velocity, acceleration, and displacement. The classification model was trained using two acoustic emission parameters (ring down count, change rate of ring down count) and the actual labels of the landslide kinematic state. Only the two acoustic emission parameters were input to the trained classifier and kinematic labels were produced through model prediction. A machine learning-based landslide displacement prediction method was developed, where landslide displacement can be automatically measured using acoustic emission data and related parameters (e.g., rainfall). Based on the output results from machine learning classification and prediction, a method for landslide early warning with graded risk was then developed, considering the response to negative circumstances such as missing data.

Finally, this article discusses the tendency to choose acoustic emission data interpretation methods for different application scenarios and alludes to the limitations and development trends of these interpretation methods. Machine learning is the current trend in acoustic emission data analysis methods, which can increase the reliability of landslide risk warning systems. In the future, a full waveform data-based acoustic emission analysis method will be introduced for landslide deformation monitoring. It is hoped that acoustic emission technology will be developed as a universal monitoring technique for soil landslide subsurface deformation.

Landslide hazards are widely distributed in China and are severely harmful. The registered landslide hazards have achieved remarkable benefits in disaster reduction through a comprehensive prevention and control system. However, approximately 80% of all geo-disasters in China still occur outside the scope of identified hazards yearly. Therefore, monitoring and early warning are important means to actively prevent landslide disasters and achieve great success in disaster mitigation owing to promptness, effectiveness, and relatively low-cost advantages. Deformation is the most significant monitoring parameter for landslides and has become a focus and general trend. Landslide deformation monitoring engineering has strict requirements for controlled cost and high reliability to achieve widespread application and accurate early warning. Therefore, the commonly used monitoring instruments focus on surface deformation and rainfall to meet the requirements for easy equipment installation and low implementation cost. However, surface deformation and rainfall are not sufficient conditions to determine the occurrence of landslides. Various challenges exist in the existing monitoring technologies and early warning methods regarding engineering feasibility and performance improvement. Thus, it is important and urgent to summarize the existing research to rationally guide future development.

The deformation monitoring methods are divided into surface and subsurface monitoring. Most surface deformation monitoring technologies are vulnerable to the interference of terrain, environment, and other factors; therefore, their timeliness and reliability are not easily guaranteed. Additionally, slope subsurface deformation monitoring technologies can directly obtain the development and damage information of the sliding surface; thus, they can recognize the disaster precursor. Subsurface monitoring has advanced early warning ability; however, the existing instruments have problems, such as high cost, small measuring range, or difficult operation. Acoustic emission technology has the advantages of low cost, high sensitivity, and continuous real-time monitoring of large deformation, and has gradually developed into an optional method for landslide subsurface deformation monitoring. Thus, efficient landslide monitoring should comprehensively use multiple technologies to overcome the limitations of a single technology, and an integrated monitoring system becomes the state-of-the-art trend. The purpose of landslide monitoring is to provide a basis for decision-making of disaster early warning, thus, avoiding casualties and property losses through effective early warning efforts. In the field of early warning, regional meteorological and individual landslide early warning methods are gradually developed and improved. Deformation monitoring data are the main basis for landslide early warning, and experts analyze the deformation trend and sudden change characteristics. Different early warning levels could be triggered by the threshold values of velocity, acceleration, or other criteria. However, a landslide has complex dynamic mechanisms and individual differences; thus, the generic early warning model needs further exploration. The intelligent early warning model integrates machine learning technology with geological engineering analysis to improve the accuracy and automation level of landslide early warning.

Deformation monitoring is essential in landslide prevention, and deformation data are the main basis for landslide early warning. Moreover, surface monitoring technologies have been widely used in the perception and decision-making process of landslides. Subsurface monitoring technologies can detect early precursors of landslide evolution to continuously improve early warning accuracy. Analyses show that early warning methods can be improved in the future by integrating machine learning models and geotechnical engineering.

Current fire smoke detectors are susceptible to many factors that can significantly affect their accuracy, such as environmental disturbances and combustion states, resulting in false alarms. Moreover, point fire detectors inevitably cause time lags for alarms due to their optical darkrooms and insect-proof nets, delaying critical rescue time. Therefore, focusing on the limitations of point carbon monoxide (CO) detectors with absorbing gas cells, this study proposes an optical probe initial fire detection system based on tunable diode laser absorption spectroscopy (TDLAS) and laser remote sensing. Then, we present a preliminary threshold-based fire alarm algorithm.

Accordingly, this study's detection system relied on TDLAS and laser remote sensing, including wavelength modulation spectroscopy, to extract CO signals from an open-path geometry. A laser (wavelength=2 331.93 nm) was also used as an optical probe to replace the traditional absorption measurement chambers, achieving a CO path-integrated volume fraction measurement under complex initial fire conditions. First, we tested the signal responses of this detection system for different CO volume fraction combinations, incidence angles, and reflectances using a standard gas cell (length=0.5 m) placed at the optical path and a standard reflector plate placed 4.4 m from the light source to examine the limitations of the system. Then, we tested the received signal power at different distances, examined the CO released from wood pyrolysis fires, calculated the corresponding integral volume fractions, and set an appropriate threshold based on the detector's limit to verify whether this optical probe fire detector could meet the requirements.

The experimental results of our limitation tests showed the following: (1) At a distance of 4.4 m and target reflectance of 0.69, the theoretical detection limit was approximately 5 (μL/L)·m, and the actual detection limit was 26.75 (μL/L)·m. (2) As the reflectance decreased, the absolute value of the signal intensity also decreased, increasing the uncertainty in the measured volume fraction by a detection limit of 88.22 (μL/L)·m and reflectance of 0.07. (3) As predicted by calculations, although the absolute value of the second harmonic signal varied slightly for the 0°-10° incidence angle, the corresponding normalized absorption peak outputs were essentially the same. (4) When the distance to the target reflective surface was within 10 m, a detection limit of no less than 20 (μL/L)·m was achieved. Conversely, the standard wood pyrolysis fire tests showed that the system with a theoretical detection limit of 30 (μL/L)·m and a proposed threshold alarm value of 70 (μL/L)·m potentially triggered the initial fire alarm.

This study determines the feasibility of an open optical path to detect initial CO release from fire through detection limit tests and standard fire tests. Overall, the proposed method integrating TDLAS and laser remote sensing achieves the expected goal of detecting initial fire, significantly addressing the shortcomings of gas detectors using absorption cavities and point fire detectors. Compared to linear beam fire detectors, this detection system does not require other cooperative targets such as reflectors than a typical wall to detect initial fire.