Field Measurement Data Research Articles

Non-linear influences of track dynamic irregularities on vertical levelling loss of heavy-haul railway track geometry under cyclic loadings

Abstract With an emphasis on the combined degradation of railway track geometry and components, a new numerical-analytical method is proposed for predicting the track geometrical vertical levelling loss (VLL). In contrast to previous studies, this research unprecedentedly considers the influence of initial track irregularities (ITI) on VLL under cyclic loadings, elastic-plastic behaviour, and different operational dynamic conditions. The non-linear numerical models are simulated using an explicit finite element package known as LS-Dyna, and their results are validated by full-scale experimental and field measurement data. The outcomes are iteratively regressed by an analytical logarithmic function that cumulates permanent settlements, which innovatively extends the effect of ITI on VLL in a long-term behaviour. For a typical heavy-haul railway operating under 30 tons axle load and 60 km/h train velocity, the result indicates that the set of ITI with the highest standard deviation (SD) of vertical profile (VP) degrades faster (37% on average) than that with the lowest SD. Additionally, our new findings reveal that the worst scenario is related to a train running at 60 km/h and carrying a load of 20 tons/axle in an uneven track whose SD of VP evolves from 3.23 mm at N = 0 (ITI) to 7.20 mm, whereas the best one corresponds to a train at 60 km/h and 30 ton axle load in an uneven track whose SD of VP downgrades from 0.48 to 1.50 mm, both at 3 M cycles (or 60 million gross tons). These findings indicate the importance of considering the ITI for predicting track geometrical VLL under cyclic loadings. Therefore, based on this research, an acceptable condition (thresholds) of ITI can be redefined for a minimum effect on VLL, which can support the development of practical maintenance guidelines to extend the railway track service life.

A decision support model for planning of spatial large extent biomass to bioenergy procurement network

This study presents a strategic-tactical decision model with two main objectives: (i) identifying the spatial distribution and estimating the harvestable biomass potential of agricultural farms within a complex landscape; and (ii) optimizing the placement of bioenergy facilities to minimize total transportation costs. Remote sensing techniques combined with spatial analyses were utilized to discriminate agricultural lands from other land-cover types. Field-data measurements were then employed to estimate the quantity of available biomass. Subsequently, a bi-level optimization model was developed to determine optimal bioenergy facility locations and calculate transportation costs. The model was applied to a 20000 km2 region in northeastern Iran. The results revealed an average annual biomass density of 4.20 Mg ha−1 from agricultural lands, corresponding to 1867 Gg of biomass across the region. The optimal solution involved placing two bioenergy facilities within a 75 km radius to collect residues biomass, resulting in a delivery cost of $16 kWh−1.

Computational Fluid Dynamics Modelling of the Temperature Distribution in Power Plant's Cooling Canal

Seawater used as a heat exchanger in the coal-fired power plant condenser system has a higher water temperature than the natural temperature of seawater. There was an increase in the temperature of the waters around the coal-fired power plant due to the discharge of boiling water reaching 6.50C. According to the regulations in force in Indonesia, the hot water temperature at the water discharge outlet should not be more than 20C of the natural temperature of seawater. The purpose of this study is to predict the pattern of temperature decrease in the temperature of the boiling water along the cooling line. The method used to predict temperature drop patterns along the cooling line uses a 3-dimensional CFD (Computational Fluid Dynamics) modelling approach with the Ansys Fluent 2023 RI application. The K-Epsilon turbulence model was chosen to describe the effect of flow turbulence on temperature changes. The modelling results were validated by comparing the actual measurements of the water temperature field data representing along the cooling line. The results showed that the performance of reducing the temperature of the water increased when passing through the cascade aeration downstream of the channel. The flow of water along the channel tends to be on the lower side, and downstream, the flow is on the left–middle side. The error calculation using MAPE resulted in 9.49%.

Development and validation of an oil palm model for a wide range of planting densities and soil textures in Malaysian growing conditions

A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia's climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy's law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1–23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82–299 palms ha−1), soil texture (27–59 % clay and 7–67 % sand), and rainfall (1800–2800 mm yr−1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, −22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.

An object-based region-growing phase unwrapping method for mapping vertical displacement in permafrost landscapes

Terrain displacement due to the seasonal thaw of the active layer above permafrost can be sensitive to climate change; however, its accurate characterization remains a challenge. This study aimed to improve the measurement of the subsidence or vertical ground surface displacement using differential synthetic aperture radar interferometry (InSAR). Existing methods for reliable phase unwrapping are hindered by the decorrelation between time-series of SAR acquisitions that can result due to the heterogeneity and structural sensitivity of permafrost landscapes to external conditions. In this study, an advanced phase unwrapping method was proposed, in which three types of regions, namely non-residue, sparse residue, and dense residue objects, were obtained from wrapped interferogram and residue map using a segmentation method. Two variants of Polynomial-Based Region Growing Phase Unwrapping (PBRGPU) were developed, which are sparse-residue Object-based PBRGPU(SOP) and dense-residue Object-based PBRGPU(DOP). The results demonstrated that the proposed method outperformed the existing phase unwrapping methods by partially suppressing the decorrelation phase and enhancing robustness for complex terrain deformation in the absence of measured field data. Both the PBRGPU variants and segmentation strategies compose the object-based unwrapping method for permafrost, and also provide a new framework by combining the segmentations and scenarios for phase unwrapping for permafrost regions.

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.

Emission of Intermediate Volatile Organic Compounds from Animal Dung and Coal Combustion and Its Contribution to Secondary Organic Aerosol Formation in Qinghai-Tibet Plateau, China.

Intermediate volatility organic compounds (IVOCs) are important precursors to secondary organic aerosols (SOAs), but they are often neglected in studies concerning SOA formation. This study addresses the significant issue of IVOCs emissions in the Qinghai-Tibetan plateau (QTP), where solid fuels are extensively used under incomplete combustion conditions for residential heating and cooking. Our field measurement data revealed an emission factor of the total IVOCs (EFIVOCs) ranging from 1.56 ± 0.03 to 9.97 ± 3.22 g/kg from various combustion scenarios in QTP. The markedly higher EFIVOCs in QTP than in plain regions can be attributed to oxygen-deficient conditions. IVOCs were dominated by gaseous phase emissions, and the primary contributors of gaseous and particulate phase IVOCs are the unresolved complex mixture and alkanes, respectively. Total IVOCs emissions during the heating and nonheating seasons in QTP were estimated to be 31.7 ± 13.8 and 6.87 ± 0.45 Gg, respectively. The estimated SOA production resulting from combined emissions of IVOCs and VOCs is nearly five times higher than that derived from VOCs alone. Results from this study emphasized the pivotal role of IVOCs emissions in air pollution and provided a foundation for compiling emission inventories related to solid fuel combustion and developing pollution prevention strategies.

A new model for predicting pressure traverse in two phase flow geothermal well

The complexity associated with the two phase or multiphase flow system have been a long-time challenge in the modelling of the flowing pressure in the production channel which includes geothermal well. The flow of hot water and its vapour ensued all throughout the geothermal system producing from the reservoir source to the wellbore and later to the surface. The performance of geothermal system is primarily dependent on the ability to accurately predict the flowing pressure in the production well. Several models derived from the fundamental thermodynamic energy equation to handle the complexity of two-phase flow in geothermal well have demonstrated diverse discrepancy when compared with real time measurement. It has been conceived in the study that the inclusion of pressure drop due to accumulation in the thermodynamic energy equation reduces the discrepancy between the predicted pressure and the field measurement. It is pertinent to the geothermal energy industry to assess the accurate model that consider all constituent terms that impact the two-phase flow behaviour in a geothermal production well. A more exact numerical model for evaluating the flowing pressure in geothermal has been derived in this study where the right constituent terms that practically impact the two-phase flow behaviour in a geothermal vertical well are considered. The present model has the capacity to predict flowing pressure accurately as the flow transit from one regime to another. Likewise, the present model was able to capture the transient period that usually occurs when the geothermal well is put to production. The present model was validated using data from KE1-22 and Mesa 6-1 wells reported in literature. The present model gave the least mean absolute relative error of 0.033 when applied to measured field data from KE1-22 Well while Hassan and Kabir, Ansari, Orkiszewski and Homogenous models gave errors of 0.12, 0.33, 0.41 and 0.38 respectively. Similarly, relative to data from Mesa 6-1 the present model gave a mean absolute relative error of 0.052 while Hassan and Kabir, Ansari, Orkiszewski and Homogenous models gave errors of 0.17, 1.46, 1.37 and 2.53 respectively. The newly derived model is reliable for assessing the performance of the geothermal system and evaluating flowing pressure at every difference transition period.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Characterisation of particle single-scattering albedo with a modified airborne dual-wavelength CAPS monitor

Abstract. Atmospheric aerosols impact the Earth's climate system directly by scattering and absorbing solar radiation, and it is important to characterise the aerosol optical properties in detail. This study reports the development and validation of an airborne dual-wavelength cavity-attenuated phase-shift (CAPS) single monitor, named A2S2 (Aerosol Absorption Spectral Sizer), based on the commercial CAPS single-scattering albedo monitor (CAPS-PMSSA; Aerodyne), to simultaneously measure the aerosol optical scattering and extinction at both 450 and 630 nm wavelengths. Replaced pressure and temperature sensors and an additional flow control system were incorporated into the A2S2 for its utilisation on board research aircraft measuring within the troposphere. The evaluation of A2S2 characteristics was performed in the laboratory and included the investigation of the signal-to-noise ratio, validation of performance at various pressure levels, optical closure studies and intercomparing with the currently validated techniques. The chamber experiments show that the A2S2 can perform measurements at sample pressures as low as 550 hPa and at sample temperatures as high as 315 K. Based on the Allan analysis results, we have evaluated that the minimum detection limit of the measurements shows that the measurements have a limit accuracy of ∼ 2 Mm−1 at 450 nm and ∼ 1 Mm−1 at 630 nm for 1 Hz measurements of both scattering coefficients (σsca) and extinction coefficients (σext). The optical closure study with size-selected polystyrene latex (PSL) particles shows that the truncation error of the A2S2 is negligible for particles with particle volume diameter (Dp) &lt; 200 nm, while, for the larger sub-micrometre particles, the measurement uncertainty of A2S2 increases but remains less than 20 %. The average factors to correct the truncation error are 1.13 and 1.05 for 450 and 630 nm, respectively. A simplified truncation correction, dependent on the scattering Ångström exponent (SAE), was developed to rectify truncation errors of the future A2S2 field measurement data. The σsca and σext measured by the A2S2 show good agreement with the concurrent measured results from the nephelometer and the CAPS particle extinction monitor (CAPS-PMex). The absorption coefficient (σabs) derived through the extinction-minus-scattering (EMS) method by the A2S2 also corresponds with the results obtained from the aethalometer. The A2S2 was successfully deployed during an aircraft measurement campaign (Atmospheric ChemistRy Of the Suburban foreSt – ACROSS) conducted in the vicinity of Paris and the surrounding regions. The average SSA measured during the entire ACROSS flight campaign is 0.86 and 0.88 at 450 and 630 nm, respectively, suggesting that light-absorbing organic aerosols play a significant role. The average SAE and absorption Ångström exponent (AAE) varied due to measurements in various pollution conditions. The results presented in this study indicate that the A2S2 instrument is reliable for measuring aerosol σsca and σext at both blue and red wavelengths, and it stands as a viable substitute for future airborne evaluations of aerosol optical properties.

Exploring the relationship between the International Roughness Index (IRI) and the ISO 8608 roughness coefficient (Gd (n 0))

ABSTRACT Globally, diverse agencies employ varied metrics to gauge road roughness, complicating the exchange of information for pavement maintenance. This study addresses this challenge by establishing a theoretical connection between the International Roughness Index (IRI) and the ISO 8608 Power Spectral Density (PSD)-based roughness coefficient, namely Gd (n 0). Based on random vibration theory, this paper first deduces a closed-form explicit solution for IRI related to Gd (n 0), solidifying a theoretical link. Next, the variability and convergence of the proposed solution are examined through Monte Carlo simulations using a quarter-car vehicle model with the Golden Car parameters. Then, we compare computed results from this study with those existing solutions from past studies, verifying that the proposed solution captures the curve trend well and agrees with the average value of existing solutions based on field measurement data. Finally, this paper proposes the IRI values corresponding to various ISO road class thresholds and a new Gd (n 0) scale for ride quality assessment of highways.

Investigation of Effective Fracture Height Using Strain Responses in a Vertical Monitoring Well during a Well Interference Test

Summary Understanding and characterizing the effective hydraulic fracture height are critical for optimizing completion design and well performance in unconventional reservoirs. Rayleigh frequency shift distributed strain sensing (RFS-DSS) data provide crucial information about the fracture geometry by measuring the strain change along the fiber. Recently, the strain data from the vertical monitoring well [VMW; Boxwood 5 Pilot Hole (B5PH)] at Hydraulic Fracture Test Site 2 (HFTS-2) was collected during a well interference test conducted on two neighboring horizontal wells [Boxwood 3H (B3H) and Boxwood 4H (B4H)] in September 2020. However, this data set was overlooked and not comprehensively analyzed. Consequently, the valuable information that this data may provide remains unexplored. The objective of this study is to determine how to interpret this vertical strain data and assess its potential to provide insights into the effective fracture height generated in the horizontal wells. To understand the measured strain data, we have developed a coupled fluid flow and geomechanics model to simulate the vertical strain responses observed during production and the well interference test. The characteristics of the strain responses are thoroughly analyzed to establish guidelines for identifying the upper and lower tips of the effective fracture in different fracture geometry scenarios. In addition, field-measured data are examined, and a history-matching process is conducted by comparing the numerical matching results with the observed data to identify the effective fracture height. Furthermore, the pressure gauge data are analyzed and compared with the strain data results to further validate the findings. The study has demonstrated that vertical strain measurements obtained from a VMW can be used to determine the effective fracture height. Large strain responses, characterized by both negative and positive peaks, are observed near the tips of the effective fractures. The upper and lower tips of the effective fracture can be identified by examining the locations of zero-strain change. The magnitude of strain response is affected by the distance between the fracture and the fiber. It is worth noting that the locations where the absolute value of the derivative of fracture conductivity changes abruptly correspond to the positions of the fracture tips. Furthermore, we study and history match the field-measured vertical strain change in HFTS-2 data sets. The pressure gauge data are analyzed to further validate our findings. The effective fracture height determined from the strain measurements falls within the range of 11,360–11,616 ft, which is consistent with the values obtained from pressure gauges (11,360–11,635 ft). Specifically, for B4H, the effective fracture height was determined to be 256 ft at a distance of 330 ft away at the time of data acquisition. Overall, the strain measurements provide valuable, time-dependent insights into the effective fracture height and the active drainage interval.

Greenhouse gas fluxes of different land uses in mangrove ecosystem of East Kalimantan, Indonesia

BackgroundMangrove ecosystems exhibit significant carbon storage and sequestration. Its capacity to store and sequester significant amounts of carbon makes this ecosystem very important for climate change mitigation. Indonesia, owing to the largest mangrove cover in the world, has approximately 3.14 PgC stored in the mangroves, or about 33% of all carbon stored in coastal ecosystems globally. Unfortunately, our comprehensive understanding of carbon flux is hampered by the incomplete repertoire of field measurement data, especially from mangrove ecosystem-rich regions such as Indonesia and Asia Pacific. This study fills the gap in greenhouse gases (GHGs) flux studies in mangrove ecosystems in Indonesia by quantifying the soil CO2 and CH4 fluxes for different land use types in mangrove ecosystems, i.e., secondary mangrove (SM), restored mangrove (RM), pond embankment (PE) and active aquaculture pond (AP). Environmental parameters such as soil pore salinity, soil pore water pH, soil temperature, air temperature, air humidity and rainfall are also measured.ResultsGHG fluxes characteristics varied between land use types and ecological conditions. Secondary mangrove and exposed pond embankment are potential GHG flux sources (68.9 ± 7.0 and 58.5 ± 6.2 MgCO2e ha− 1 yr− 1, respectively). Aquaculture pond exhibits the lowest GHG fluxes among other land use types due to constant inundation that serve as a barrier for the release of GHG fluxes to the atmosphere. We found weak relationships between soil CO2 and CH4 fluxes and environmental parameters.ConclusionsThe data and information on GHG fluxes from different land use types in the mangrove ecosystem will be of importance to accurately assess the potential of the mangrove ecosystem to sequester and emit GHGs. This will support the GHG emission reduction target and strategy that had been set up by the Indonesian Government in its Nationally Determined Contributions (NDC) and Indonesia’s 2030 Forest and Other Land Use (FOLU) Net Sink.

Field measurement data set of wind turbine tower for enhanced calculation of vortex-induced vibration

Field measurement data set of wind turbine tower for enhanced calculation of vortex-induced vibration

Automated classification of drill string vibrations using machine learning algorithms

Downhole vibration can cause drill string failures and significant economic losses. Recognizing harmful vibrations in real time is highly significant for optimizing drilling operations. This work aims to identify whirls and stick-slip using machine learning (ML) from downhole measured accelerations. A rotor dynamics model is utilized to simulate whirl and a coupling method between whirl and stick-slip is presented. 448 sets of simulated acceleration data are used for the training of ML classification models. 14 features for whirl identification and 8 for stick-slip are defined in each acceleration. The generalization abilities of the trained whirl and stick-slip classifiers are validated using ABAQUS simulation data and field-measured data, respectively. Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Neural Network algorithms are tested. The classification accuracy is highest when whirl is divided into synchronous forward whirl, synchronous backward whirl, and discontinuous contact whirl. Both SVM and Neural Network algorithms can achieve nearly 100% accuracy in rotor dynamics data and ABAQUS data. The highest accuracy is attained by utilizing both tangential and radial acceleration. However, using only tangential acceleration can still fulfill engineering requirements. The stick-slip classifier, when using SVM and Neural Network, can achieve 100% recognition accuracy on all test data. The proposed methods can lessen sensor need for downhole measurements and speed up data processing, and may be applied to other problems involving rotating soft shafts.

Effect of shade on simultaneous estimation of non-photosynthetic and photosynthetic vegetation cover using the NDVI-NSSI normalized difference triangular space

ABSTRACT The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV.

Three-dimensional acoustic propagation of noise from impact pile driving in a complex costal environment and its effects on large yellow croaker (Pseudosciaena crocea)

The effects of high-intensity impulsive noise generated by pile driving on fish are a major concern in environmental impact assessments. Numerical acoustic models are essential for predicting underwater-acoustic-related problems in complex coastal environments prior to offshore construction. However, underwater noise modeling for impact pile driving has often been performed using simplistic propagation models that are inadequate for three-dimensional (3D) environments. A 3D parabolic equation method (PE) was established in this study to better predict broadband transmission loss (TL) from impact pile driving in complex coastal environments and its influence on the large yellow croaker (Pseudosciaena crocea). The effects of 3D propagation were investigated using two realistic scenarios with different bathymetric complexities. The values and attenuation rate of the broadband TL for the steeply sloped bottom were significantly greater than those for the flat and weakly varying bottoms over 3 km. At a water depth of 5 m, a difference of approximately 10 dB was observed between the two TL scenarios at a distance of 4.5 to 5 km. The simulation results are in reasonable agreement with the field measurement data, with a difference of less than 3 dB. The zones of behavioral response and injury in the large yellow croaker were estimated using the For3D model. The results showed that the effects of the noise generated by the impact pile driving on the large yellow croaker were evident and three-dimensional. Therefore, 3D propagation effects should be considered when analyzing the influence of underwater noise on marine animals.

Nonlinear Fourier classification of 663 rogue waves measured in the Philippine Sea.

Rogue waves are sudden and extreme occurrences, with heights that exceed twice the significant wave height of their neighboring waves. The formation of rogue waves has been attributed to several possible mechanisms such as linear superposition of random waves, dispersive focusing, and modulational instability. Recently, nonlinear Fourier transforms (NFTs), which generalize the usual Fourier transform, have been leveraged to analyze oceanic rogue waves. Next to the usual linear Fourier modes, NFTs can additionally uncover nonlinear Fourier modes in time series that are usually hidden. However, so far only individual oceanic rogue waves have been analyzed using NFTs in the literature. Moreover, the completely different types of nonlinear Fourier modes have been observed in these studies. Exploiting twelve years of field measurement data from an ocean buoy, we apply the nonlinear Fourier transform (NFT) for the nonlinear Schrödinger equation (NLSE) (referred to NLSE-NFT) to a large dataset of measured rogue waves. While the NLSE-NFT has been used to analyze rogue waves before, this is the first time that it is systematically applied to a large real-world dataset of deep-water rogue waves. We categorize the measured rogue waves into four types based on the characteristics of the largest nonlinear mode: stable, small breather, large breather and (envelope) soliton. We find that all types can occur at a single site, and investigate which conditions are dominated by a single type at the measurement site. The one and two-dimensional Benjamin-Feir indices (BFIs) are employed to examine the four types of nonlinear spectra. Furthermore, we verify on a part of the data set that for the localized types, the largest nonlinear Fourier mode can be attributed directly to the rogue wave, and investigate the relation between the height of the rogue waves and that of the dominant nonlinear Fourier mode. While the dominant nonlinear Fourier mode in general only contributes a small fraction of the rogue wave, we find that soliton modes can contribute up to half of the rogue wave. Since the NLSE does not account for directional spreading, the classification is repeated for the first quartile with the lowest directional spreading for each type. Similar results are obtained.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

Shoreline evolution in adjacent to a coastal structure in Hiep Thanh commune, Duyen Hai district, Tra Vinh province

Coastal erosion is significant along the coastline in Hiep Thanh commune, Duyen Hai district, Tra Vinh province, Viet Nam. In order to address this severe coastal erosion, the local community has constructed a concrete embankment as an emergency solution to protect the coastline. However, this solution exacerbates the issue since the fundamental processes of coastal erosion remain inadequately explored and understood. Specifically, in beach sections without the concrete embankment, erosion still occurs. Therefore, this paper aims to investigate the shoreline changes adjacent to the embankment using an integrated approach with remote sensing and diffusion theory. The results show that beach erosion is occurring near the embankment at a rate of 44 meters per year, and the diffusion coefficient for longshore sand transport in the study area is 130 square meters per day. This study offers a practical approach to examining changes in the shoreline adjacent to a coastal structure, particularly valuable in situations where there is a scarcity of measured field data.