Construction and Application of an Emergency Monitoring Indicator Evaluation Model Based on the Spatiotemporal Evolution of Forest Fires

A comprehensive investigation into the aero-thermodynamic impacts of UAV-generated airflow on the rice microclimate is essential to elucidate the complex relationships between wind speed, temperature, and temporal dynamics during the critical growth stages of rice. Focusing on the vulnerable stages of rice such as heading, panicle, and flowering, this research aims to advance the understanding of microclimatic influences on rice crops, thereby informing the development of UAV-based strategies to enhance crop resilience and optimize yields. By utilizing UAV rotor downwash, the research examines wind temperature and speed at three key diurnal intervals: 9:00 a.m., 12:00 p.m., and 3:00 p.m. At 9:00 a.m., UAV-induced airflow creates a stable microclimate with favourable temperatures (27.45–28.45 °C) and optimal wind speeds (0.0700–2.050 m/s), which promote and support pollen transfer and grain setting. By 12:00 p.m., wind speeds peak at 2.370 m/s, inducing evaporative cooling while maintaining temperature stability, yet leading to some moisture loss. At 3:00 p.m., wind temperatures reach 28.48 °C, with a 72% decrease in wind speed from midday, effectively conserving moisture during critical growth phases. The results reveal that UAV airflow positively influences panicle and flowering stages, where carefully moderated wind speeds (up to 3 m/s) and temperatures reduce pollen sterility, enhance fertilization, and optimize reproductive development. This highlights the potential of UAV-engineered microclimate management to mitigate stress factors and improve yield through targeted airflow regulation. Key agronomic parameters showed significant improvements, including stem diameter, canopy temperature regulation, grain filling duration, productive tillers (increasing by 30.77%), total tillers, flag leaf area, grains per panicle (rising by 46.55%), biological yield, grain yield (surging by 70.75%), and harvest index. Conclusively, optimal aero-thermodynamic effects were observed with 9:00 a.m. rotor airflow applications during flowering, outperforming midday and late-afternoon treatments. Additionally, 12:00 p.m. airflow during flowering significantly increased the yield. The interaction between rotor airflow timing and growth stage (RRS × GS) exhibited low to moderate effects, underscoring the importance of precise timing in maximizing rice productivity.

Air pollution is one of the most challenging issues poses serious threat to human health and environment. The increasing influx of population in metropolitan cities has further worsened the situation. Quantifying the air pollution experimentally is quite a challenging task as it depends on many parameters viz., wind speed, wind temperature, relative humidity, temperature etc. It requires the investment of huge money and manpower for controlling air pollution. Machine learning technique-based computer modelling reduces both of the parameters. In the present work, the dependence of air pollution level on wind speed and temperature has been taken up using machine learning in the form of ANN and LSTM model. The recorded data of air pollution level (PM2.5) is collected from a measurement station of Lucknow city situated at Central School, CPCB. The data is used in an Artificial Neural based network and in an LSTM model to predict suitably the level of air pollution for a known value of average wind speed and temperature without experimental measurements. LSTM model is found to predict the pollution level better than ANN for the developed ANN networks.

In this research we investigate the relationships between wind speed and temperature time series data in Bangkok, Thailand, from the time interval of January 2009 to December 2011 using wavelet transform (WT), cross wavelet transform (XWT) and wavelet coherence (WTC). The results from all three wavelet analysis show the strong periodicity around period 1 day (hourly data) and period band 256-450 days (daily data) variations that are exhibited in both wind speed and temperature data across the entire power spectrum from 2009 to 2011. These two oscillations are connected with the natural day time effects and the annual natural season cycle. Although the daily periodic for the temperature is appeared nearly uniform all year but it is not the case for wind speed. In 2009 this wind speed oscillations appear only from mid-February to mid-April in summer and from the fourth week of May to the third week of August in rainy season. XWT also detects strong high common power between the wind speed and temperature at a period band of 14-25 days in summer 2009, a period band of 4-8 days in summer 2009, July 2009, summer 2010 and summer 2011. WTC shows the coherence period band around 10-30 days appeared in summer and rainy season and 32-50 days in summer 2009 and rainy season in 2010. From these three wavelet analysis, the wind speed and temperature time series data show the strong correlation especially at 1 day and 256-450 days period band and also at several different scales. This studied will be helpful in predicting the wind speed and temperature for the future used.

Рассмотрен новый подход к прогнозированию распространения пожаров в сельских населенных пунктах. Показано, что динамика развития процессов (пожаров) и явлений носит нелинейный, а зачастую хаотичный (непредсказуемый) характер, что обуславливает необходимость поиска альтернативных методов моделирования с применением нестандартных математических аппаратов. Использована теория перколяции для получения модели процесса горения. При оценке пожарной опасности объектов, расположенных на открытых территориях, применена функция нейронных сетей. Показано, что описание развития пожара на основе перколяционного процесса с применением функции нейронной сети с учетом определенных данных позволит выполнить оценку пожарной опасности при проектировании строительных объектов.

In recent years, it has been observed that numerous cases of windstorm event. There are many factors that cause windstorms to occur. The factors of meteorology, urban morphology, the topography need to be studied to find out the cause of windstorms. The work analyzed meteorology factors such as: wind speed, humidity and temperature, occurring in 2017. Meteorological data from the Department of Environment Malaysia (DOE) station, allow determining the wind speed, humidity and temperature data daily in 2017. As well as, the pattern of parameter and their relationship between them being determined. The pattern of wind speed monthly was inconsistent. The highest average wind speed in 2017 was 4.72 m/s and the lowest average was 0.56 m/s. While humidity, the highest average was 83.12 % and the lowest average humidity was 72.33 %. For temperature, the maximum average for 2017 was 28.43 °C and the minimum average was 26.26 °C. The correlation of wind speed between humidity and temperature was -0.256 and 0.278, which is low correlate. That must be other active factors that influence the wind speed and contribute to the windstorm event. Wind speed, humidity and temperature during the windstorm event on 11 February 2017 was analyzed. During the windstorm event, the wind speed blows up to 15.7 m/s while the humidity reading decrease to 68.4 % and the temperature was 30.9 °C. When the wind speed reading is high, the temperature reading also increases and the humidity reading will go down and vice versa and has caused the windstorm event to happen.

The alarming problems related to illegal construction in individual enterprises have begun to raise questions about the effectiveness of internal control in these enterprises. In order to better achieve the internal control objectives, a set of scientific and complete rating index systems and evaluation models were constructed. This paper first proposes a fuzzy comprehensive evaluation method for enterprise internal control compliance risks, establishes a factor set, the weight allocation judged by the analytic hierarchy process, and builds the internal control evaluation model framework through the hierarchical decomposition method. Secondly, the core indicators in compliance internal control are determined by the form-filling method, the core indicators are filled into the framework determined by the hierarchical decomposition method, and finally, the weights of each indicator in each level are determined through weighting calculation, and the model is built. Through the specific application of an enterprise, it is found that the effectiveness of internal control has developed from “qualified” to “excellent” in the past five years, and the POOR value has shown a downward trend year by year, from a high of 0.2821 to 0.0717. The only thing that has not developed to excellent is that in 2022, the enterprise did not carry out construction under the provisions of the planning permit and was fined 675,900 yuan, and the grade was downgraded to “good”, but the overall degree of realization of the company’s internal control risk rating is improving year by year.

This work presents the performance evaluation of the Weather Research and Forecasting (WRF) model to estimate surface wind speed and direction, air temperature, and water vapor mixing ratio considering 22 configurations at high spatial resolution (1 km) during one week in winter and one week in spring, in order to determine the best-performing schemes for air quality purposes in the Metropolitan Area of Buenos Aires, Argentina. Results show that the use of urban schemes mostly affects wind speed and temperature. The single-layer urban canopy model (UCM) coupled with the Boulac planetary boundary layer (PBL) scheme exhibits the best results for wind speed. Wind direction and water vapor mixing ratio are more sensitive to the land surface model scheme, with results slightly improving with the Noah-MP land surface model. Wind speed and direction errors are larger when the former is lower. When removing from the analysis wind speed values below 2.6 m s–1 for the winter week and 3.1 m s–1 for the spring week, the root mean square errors for wind direction decreased between 50 and 72% of the original value, depending on the configuration and week. Overall, under the studied conditions, configurations including Noah-Mp land surface model or the combination of a simple UCM with BouLac PBL are suitable for air quality applications, as they reproduce both temperature and water vapor mixing ratio relatively well, with errors below 10% and Correlation values above 0.7, and are the best performing configurations for wind direction and speed, respectively.

The transfer system has an extremely important influence on the operation management and economic benefits of the whole rail network. The paper selects evaluation indexes based on the analysis of transfer system time value influencing factors, excludes part of the indexes by using importance analysis and correlation analysis, and constructs the evaluation index system of rail transit transfer system using the STATA 16 software. Using a combination of the analytic hierarchy process (AHP) and CRITIC method, the evaluation indexes were comprehensively assigned. The time value evaluation model was established based on the matter-element extension evaluation model. Finally, Wuhan rail transit transfer stations Dazhi Road Station and Xunlimen Station are selected as examples for empirical analysis, and improvement measures are proposed. Unlike previous studies, this study introduces time value as a core indicator and uses a matter-element extension evaluation model for evaluation. Empirical analyses show that the proposed evaluation index system based on time value can better reflect the passenger experience as well as the efficiency of the transfer system. The selected matter-element extension evaluation model can better deal with the uncertainty between indicators and solve the multi-objective contradiction problem. The evaluation results of the model are consistent with the actual research results of the transfer station, and the evaluation model has better applicability.

The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model’s generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as “good” quality comprised 11.18% of the total, while “medium” quality forests constituted the majority at 76.77%. In contrast, “poor” quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies.

The spawning habitats of anchovy Engraulis encrasicolus and sardine Sardinops sagax in the southern Benguela upwelling ecosystem were characterised by comparing their egg abundances with environmental variables measured concomitantly during two different survey programmes: the South African Sardine and Anchovy Recruitment Programme (SARP), which comprised monthly surveys conducted during the austral summers of 1993/94 and 1994/95; and annual pelagic spawner biomass surveys conducted in early summer (November/December) from 1984 to 1999. Eggs were collected using a CalVET net. Physical variables measured included sea surface temperature (SST), surface salinity, water depth, mixed-layer depth, and current and wind speeds; biological variables measured included phytoplankton biomass, and zooplankton biomass and production. Spawning habitat was identified by construction of quotient curves derived from egg abundance data and individual environmental variables, and relationships between these variables were determined using multivariate co-inertia analysis. SARP data showed that anchovy spawning was associated with cool water and moderate wind and current speeds, whereas sardine spawning was related to warmer water and more turbulent and unstable conditions (i.e. high wind speeds and strong currents) than for anchovy. SARP data also showed significant differences in selection of spawning habitat of the two species for all environmental variables. The relationship between anchovy egg abundance and salinity was strongly positive, but strongly negative with water depth, phytoplankton biomass and zooplankton production. Sardine egg abundance was strongly positively related to current speed. The spawner biomass survey data demonstrated that the spawning habitat of anchovy was characterised by warm water and high salinity, whereas sardine spawning was associated with cool water and low salinity. The survey data showed significant differences in spawning habitat selection by anchovy and sardine for SST, salinity and zooplankton biomass, but not for the other environmental variables. There was a positive relationship between anchovy egg abundance and SST, salinity and mixed-layer depth, and a negative relationship with water depth, phytoplankton biomass and zooplankton production. For sardine there was a strong positive relationship between egg abundance and current speed and wind speed. Differences in the results between the two survey programmes could be attributable to differences in their spatio-temporal coverage. Spawning habitats of anchovy and sardine appear to be substantially different, with anchovy being more specific than sardine in their preference of various environmental conditions.

<p>The number of severe weather events at the Arctic region increased significantly. Its formation related generally to the mesoscale processes including downslope windstorms over Novaya Zemlya, Svalbard, Tiksi bay accompanied by strong winds. Therefore, its investigation required detailed hydrometeorological and climatic information with a horizontal resolution of at least several kilometers. This work aims to investigate extreme wind speeds statistics associated with downslope windstorms and evaluate it according to the COSMO-CLM Russian Arctic hindcast, ASR reanalysis, stations and satellite data.</p><p>COSMO-CLM Russian Arctic hindcast created in 2020 covers the 1980–2016 period with grid size ~12 km and 1-hour output step, containing approximately a hundred hydrometeorological characteristics, as well at surface, as on the 50 model levels. The primary assessments of the surface wind speed and temperature fields showed good agreement with ERA-Interim reanalysis in large-scale patterns and many added values in the regional mesoscale features reproduction according to the coastlines, mountains, large lakes, and other surface properties.</p><p>Mean values, absolute and daily maxima of wind speed, high wind speed frequencies were estimated for the COSMO-CLM Russian Arctic hindcast and the well-known Arctic System Reanalysis (ASRv2) for a 2000-2016 period. COSMO-CLM showed higher mean and daily maximal wind speed areas concerned to coastal regions of Svalbard and Scandinavia, over the northern areas of Taymyr peninsula. At the same time, the absolute wind speed maxima are significantly higher according to ASRv2, specially over the Barents Sea, near the Novaya Zemlya coast (differences are up to 15-20 m/s). The same pattern observed by a number of days with wind speed above the 30 m/s threshold. Compared with station data, the ASRv2 reproduced mean wind speeds better at most coastal and inland station, MAE are within 3 m/s. For absolute wind speed maxima differences between two datasets get lower, the COSMO-CLM hindcast is quite better for inland stations.</p><p>Model capability to reproduce strong downslope windstorms evaluated according to the observations timeseries over Novaya Zemlya, Svalbard and Tiksi stations during bora conditions. Generally, the ASRv2 reproduced the wind direction closer to observations and the wind speed worser than COSMO-CLM. The extreme wind speed frequencies during bora cases have less errors according to COSMO-CLM hindcast (up to ~5%) compared to the ASRv2 data (up to 10%). At the same time, moderate wind speed frequencies are reproduced by ASRv2 better.</p><p>Five specific Novaya Zemlya bora cases were evaluated according to SAR satellite wind speed data. Both ASRv2 and COSMO-CLM overestimated mean wind speed (MAE 0.5-6 m/s), maximal wind speed bias has different signs, however, the COSMO-CLM is better in most cases. Extreme percentiles biases (99 and 99.9%), correlation, structure and amplitude (according to the SAL method) are closer to observations by the COSMO-CLM hindcast.</p>

The interaction between ventilation and fire development in diagonal pipe networks makes the study of temperature characteristics extremely complex. The thermodynamic effect caused by high temperature will change the original ventilation state, cause smoke flow retrogression and airflow reversal, and expand the disaster range. Therefore, exploring the temperature attenuation characteristics in diagonal pipe networks is necessary. In this article, the temperature distribution and attenuation in a diagonal pipe network are studied using the numerical simulation method based on the theoretical model of temperature attenuation in a single roadway. In the diagonal branch, the St number in the temperature attenuation model is optimized. The temperature attenuation of the left and right paths can be divided into two stages. The optimal St number of the temperature attenuation model under different wind speeds in the left way is determined. The fitting relationship of wind speed, distance, and temperature in the first stage of the right way is established, and the fire source distance in the second stage of the right way has the most significant influence on the temperature attenuation by using the method of multivariate statistics. The temperature of the smoke backflow front in the left and right paths decreases gradually with the increase in the fire source, and the temperature of the smoke backflow front in the left way is higher than that in the right way.

In this study, the impacts of both temperature and wind speed trends on reference evapotranspiration have been assessed using as a case study the Southern Italy, which present a wide variety of combination of such climatic variables trends in terms of direction and magnitude. The existence of statistically significant trends in wind speed and temperature from observational datasets, measured in ten stations over Southern Italy during the period 1968–2004, has been investigated. Time series have been examined using the Mann–Kendall nonparametric statistical test in order to detect possible evidences of wind speed and temperature trends at different temporal resolution and significance level. Once trends have been examined and quantified, the effects of these trends on seasonal reference evapotranspiration have been evaluated using the FAO-56 Penman–Monteith equation. Results quantified the effects of extrapolated temperature and wind speed trends on reference evapotranspiration. Where these climatic drivers are on the same direction, reference evapotranspiration generally increases during the growing season due to a nonlinear overlapping of effects. Whereas wind speed decreases and temperature increases, there is a sort of counterbalancing effect between the two considered climatic forcing in determining future reference evapotranspiration.

One of the vital climatic parameters with significant roles in many natural phenomena is wind. The importance of wind cannot be overemphasized due to its role as a source of renewable energy. The understanding of wind is of great importance particularly for the purpose of prediction and management of severe weather events. However, wind as a climatic parameter depends on relative humidity and temperature as well as other weather parameters and several statistical approaches such as time series analysis, extreme value analysis and spatial analysis have been used to analyze wind speed data. This study uses the kernel density method in analyzing wind speed in Ughelli, Delta State and its connection with relative humidity and temperature using the Gaussian kernel function for a period of five consecutive years from 2018 to 2022. The performance measure employ is the asymptotic mean integrated squared error (AMISE) with the Pearson R test that measures the strength of the relationship that exists between parameters. The results of the investigation with regards to the AMISE shows that 2018 recorded best performance with wind speed and relative humidity while 2021 recorded best performance for wind speed and temperature but 2019 recorded unsatisfactory outcomes for wind speed and the two parameters. This implies that human activities that depend on these parameters for their performance did best in 2018 and 2021 respectively. Furthermore, in terms of connectivity, wind speed and relative humidity are negatively correlated in 2018 and 2022 but positively correlated in 2019, 2020 and 2021 while wind speed and temperature are negatively correlated which implies that as temperature increases, wind speed decreases.

The insulated-gate bipolar transistor (IGBT) is the weakest component in wind power system, of which lifetime mainly relies on its operation environment, especially for those in generator side of wind turbine where higher failure rate is expected, resulted from fluctuation of wind speed greatly. However, in most literatures, wind speed is just simplified as constant value, and then linear accumulation method is applied to evaluate the life of IGBT module in different wind farms, hardly obtain the relationship between wind speed distribution and lifetime consumption of IGBT module, let alone pertinent thermal lifetime management strategies. Therefore, in the paper, a probabilistic lifetime evaluation model of IGBT module in generator side (GSI) of doubly fed induction generator (DFIG) considering wind turbulence and nonlinear damage accumulation effect is proposed. Firstly, approach to calculate the mean time to failure (MTTF) of IGBT module based on nonlinear damage accumulation is presented; then, a probabilistic lifetime evaluation model is built and relationship between wind speed probability and lifetime consumption of GSI in three commercial wind plants is also analyzed. Furthermore, thermal lifetime extension measures are proposed based on the built multi-scale zones life evaluation model. Results indicate that the largest destructive point is close to synchronous wind speed, and the vulnerable zone of wind speed range is that higher than synchronous wind speed, with small probability density on the contrary. Based on the conclusion, new methods to extend lifetime of IGBT module in generator side converter (GSC) are presented through a case study, as well as validation of the proposed strategies.