Set Of Indices Research Articles

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas.

Quantification of uncertainty in a defect-based Physics-Informed Neural Network for fatigue evaluation and insights on influencing factors

Substantial advances in fatigue estimation of defective materials can be attained through the employment of a Physics-Informed Neural Network (PINN). The fundamental strength of such a framework is the ability to account for several defect descriptors while maintaining predictions physically sound. The first objective of the present work is the assessment of the PINN estimated fatigue life variability due to uncertainties carried by the inputs. Additionally, a set of sensitivity indices are employed to explore the influence of defect descriptors in fatigue life. The work suggested that some traditionally neglected defect descriptors may play a relevant role under specific circumstances.

Validity of a novel optical coherence tomography angiography flow index in a cohort of primary open angle glaucoma

BackgroundVascular mechanisms are implicated in many ocular diseases. Therefore, different vascular imaging modalities are used in management of such conditions. Optical coherence tomography angiography (OCTA) has high spatial resolution and segmentable 3D volumetric sampling enabling isolation of retinal and peripapillary vascular beds. However, OCTA only indirectly derives quantitative flow data i.e. velocimetry through methods and algorithms liable to limitations like signal saturation. This study introduces and validates novel mathematical OCTA flow indices that may compensate for some OCTA velocimetric limitations.MethodsThirty-seven eyes of 23 POAG patients were included. Each underwent baseline and follow-up assessment one month thereafter. Assessment comprised full ophthalmological examination, intraocular pressure (IOP), systemic arterial blood pressure (SABP) and OCTA macula and ONH. Angiograms were processed using ImageJ to calculate OCTA intensity-based flow indices (FIOs), for superficial vascular plexus (SVP), deep vascular plexus (DVP) and optic nerve head vascular plexus (ONH-RPC), i.e. SFIO, DFIO and ONHFIO respectively. Mean ocular perfusion pressure (MOPP) was calculated using IOP and SABP. OCTA vascular densities (VD) and MOPP were used to calculate three respective mathematical flow indices (FIMs) for SVP, DVP and ONH-RPC, based on Hagen-Poiseuille law, i.e. SFIM, DFIM, ONHFIM respectively. Pearson test was used for correlation between the two sets of indices. Intraclass correlation coefficient (ICC) was tested for baseline and follow-up values for each index.ResultsThere was positive correlation between the three FIMs and their respective FIOs at baseline and follow-up ranging between high and moderate. Correlation coefficients (CCs) were 0.773 and 0.609 for SFIM and SFIO P-value < 0.001, 0.829 and 0.624 for DFIM and DFIO P-value < 0.001 and 0.516 and 0.737 for ONHFIM P-value = 0.001 for baseline and follow-up respectively. ICCs were 0.772 P-value < 0.001, 0.328 P-value = 0.022 and 0.888 P-value < 0.001 for SFIM, DFIM and ONHFIM respectively. For SFIO, DFIO and ONHFIO, ICCs were 0.420 P-value = 0.004, 0.079 P-value = 0.320 and 0.833 P-value < 0.001 respectively.ConclusionThe novel FIMs are reliable alternatives to FIOs and may compensate for OCTA signal saturation in extremes of MOPP. SFIM and ONHFIM showed high ICCs with excellent reliability. While DFIM demonstrated low ICC indicating poor reliability, it still performed better than its corresponding DFIO.

On the complementarity of wave, tidal, wind and solar resources in Ireland

The use of renewable energy sources has been recognised as a key strategy for combating anthropogenic climate change. These energy sources are regarded as sustainable because they are naturally replenished and do not produce greenhouse gases. A vital step in achieving a low-carbon economy and addressing the global challenge of climate change is the implementation of renewable energy alternatives. This “green revolution” has been led by solar and wind energy. Incorporating new forms of renewable energy resources, such as wave and tidal energy, into the current mix of resources will aid in the transition to a fully 100% renewable energy future due to the abundance of such resources [1]. The complementarity assessment of renewable energy resources is crucial to design the optimal mix of these resources to meet load requirements in a jurisdiction. Multiple studies in the literature discuss the complementarity of renewable energy modalities for different jurisdictions. A review of such studies is presented in [2]. The review shows that most of these studies focus on wind, solar and hydro-power generation, with most focusing on just two of these three modalities. However, recent efforts have been made to assess the temporal complementarity of more than two resources, including marine renewable energy resources (wave and tidal) for US and UK jurisdictions [3], [4], concluding that marine renewable resources may have significant value to a future power system in terms of reduced balancing requirements and valuable capacity contribution. Similarly, Ireland may enjoy similar benefits from combined resource exploitation due to its island topography and good marine resource. Fig. 1, by way of example, presents renewable resource generation profiles (hourly resolution) for four seasonal weeks in 2017 and illustrates the potential benefits of combining marine resources at Inishtrahull Sound, Ireland. For example, wind and tidal are low in the autumn week (highlighted section), while wave and solar resources are available. However, the summer week sees low wind and wave resources, while tidal and solar provide complementary benefits. Another critical aspect of the complementarity studies is the set of metrics and indices used to assess the complementarity. Such metrics and indices are reviewed in [5]. Correlation coefficients are commonly used in the literature to evaluate complementarity between energy sources. However, there are several issues related to the correlation metrics reported in the literature, including the inability to handle nonlinearities in the data and the inability to handle more than two resources [6], to name a few. &#x0D; In this paper, we present a complementarity assessment of the four renewable resources, i.e. wave, tidal, wind and solar, around the island of Ireland (including Northern Ireland) using the new complementarity indices based on the mathematical concept of the total variation [6], which allow for complementarity assessment of more than two resources. We also comment on the possible benefits of the temporal characteristics of the marine renewable resources on the Irish generation mix. &#x0D; &#x0D; Please see attached file for Figures and references.&#x0D;

Short-Term Reliability Assessment of Integrated Power-Gas Systems With Hydrogen Injections Using Universal Generating Function

Injecting alternative gas (e.g., green hydrogen from wind generation) into the gas system is a promising way to decarbonize the energy sector. However, the fluctuating renewable generation could lead to time-varying alternative gas injections. Then, the gas composition in the gas system may become uncertain, increasing the short-term risk of the integrated power-gas systems during the operation. This paper proposes a short-term reliability assessment approach for IPGS with alternative gas injections. First, the multi-performance and multi-state universal generating functions (UGF) are constructed to efficiently model the short-term reliability of the power-to-gas facility and the wind farm, respectively. Then, a reliability management UGF operator is proposed to minimize both load shedding and gas security violation. A set of novel reliability indices is proposed to comprehensively assess the gas security violations under uncertain gas compositions. Moreover, an analytical short-term reliability assessment approach is proposed, where the state-based sequential approximation and state-based McCormick envelope techniques are tailored and embedded. By optimizing the solution order by the system states, the nonconvexities in the reliability management optimization problem can be handled tractably without increasing the computation burden. Finally, the IEEE 24 bus Reliability Test System and the Belgium gas system are used to validate the proposed approach.

Linking reported drought impacts with drought indices, water scarcity and aridity: the case of Kenya

Abstract. The relation between drought severity and drought impacts is complex and relatively unexplored in the African continent. This study assesses the relation between reported drought impacts, drought indices, water scarcity and aridity across several counties in Kenya. The monthly bulletins of the National Drought Management Authority in Kenya provided drought impact data. A random forest (RF) model was used to explore which set of drought indices (standardized precipitation index, standardized precipitation evapotranspiration index, standardized soil moisture index and standardized streamflow index) best explains drought impacts on pasture, livestock deaths, milk production, crop losses, food insecurity, trekking distance for water and malnutrition. The findings of this study suggest a relation between drought severity and the frequency of drought impacts, whereby the latter also showed a positive relation with aridity. A relation between water scarcity and aridity was not found. The RF model revealed that every region, aggregated by aridity, had their own set of predictors for every impact category. Longer timescales (≥ 12 months) and the standardized streamflow index were strongly represented in the list of predictors, indicating the importance of hydrological drought to predict drought impact occurrences. This study highlights the potential of linking drought indices with text-based impact reports while acknowledging that the findings strongly depend on the availability of drought impact data. Moreover, it emphasizes the importance of considering spatial differences in aridity, water scarcity and socio-economic conditions within a region when exploring the relationships between drought impacts and indices.

Adaptive Aromaticity in 18e Metallapentalenes.

Complexes with aromaticity in both the lowest singlet state (S0) and the lowest triplet state (T1) (denoted as adaptive aromaticity) are rare because according to Hückel's and Baird's rules, a species could be aromatic in either the S0 or T1 state in most cases. Thus, it is particularly challenging to design species with adaptive aromaticity. Previous reports on adaptive aromaticity were mainly focused on 16e metallapentalenes. Here, we demonstrate that 18e metallapentalenes could possess adaptive aromaticity supported by a set of aromaticity indices when the nitrido and imido ligands are introduced via density functional theory calculations. Further investigation suggests that the metal-carbon bond strength plays an important role in the S0 state aromaticity and the T1 state aromaticity could be attributed to spin electron localization. All these findings could be useful for the development of metallaaromatic chemistry.

Local climate zone mapping using remote sensing: a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime light data

ABSTRACT The local climate zone (LCZ) scheme has been widely utilized in regional climate modeling, urban planning, and thermal comfort investigations. However, existing LCZ classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments. In this study, we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations, i.e. daytime Ziyuan-3 stereo imageries (2.1 m) and Luojia-1 nighttime light (NTL) data (130 m). Firstly, we performed land cover classification using multiple machine learning methods (i.e. random forest (RF) and XGBoost algorithms) and various features (i.e. spectral, textural, multi-view features, 3D urban structure parameters (USPs), and NTL). In addition, we developed a set of new cumulative elevation indexes to improve building roughness assessments. The indexes can estimate building roughness directly from fused point clouds generated by both along- and across-track modes. Finally, based on the land cover and building roughness results, we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping. The results for Beijing, China, show that our method yielded satisfactory accuracy for LCZ mapping, with an overall accuracy (OA) of 90.46%. The overall accuracy of land cover classification using 3D USPs generated from both along- and across-track modes increased by 4.66%, compared to that of using the single along-track mode. Additionally, the OA value of LCZ mapping using 2D and 3D USPs (88.18%) achieved a better result than using only 2D USPs (83.83%). The use of NTL data increased the classification accuracy of LCZs E (bare rock or paved) and F (bare soil or sand) by 6.54% and 3.94%, respectively. The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Geostatistical analysis of the relationship among geomorphology, land cover, and AOD at the local scale in a South American desert region

This work aims to assess the particle matter distribution in Tulum Valley, San Juan, Argentina, during 2019, in relation to geomorphology. Random forest supervised classifications were performed to obtain customized annual landform and seasonal land cover maps using Landsat 8 Surface Reflectance (OLI); Sentinel-1 SAR, SRTM digital elevation images, and a set of spectral indices. Geostatistical analyses, including Linear Mixed Models, were performed considering the ln of the mean, the ln of the standard deviation, and the Angstrom Exponent of the MAIAC Aerosol Optical Depth product as response variables, and landforms, landforms + land cover, Land Surface Temperature, and Moisture Stress Index as explanatory variables. The models allowed statistically significant differentiation of the Aerosol Optical Depth means of the landform and landform + land cover classes. The percentage of residual standard error for the means obtained from the model was up to 4.02%. The Angstrom Exponent values below 0.0858, along with the spatial distribution of the three response variables, indicate that the primary aerosol source in the study area is wind-driven soil/sediment erosion, which shows a strong relationship with emission areas. No long-distance transport of aerosols was observed.

Research on the cultivation mechanism of “double-creative” talents in ethnic regions with the integration of industry and education in the context of the Internet

Abstract In the context of the rapid development of the Internet and big data technology, it is both an opportunity and a challenge for cultivating “dual-innovation” talents in ethnic regions. This paper proposes a fuzzy hierarchical analysis-fuzzy comprehensive evaluation model to study the cultivation mechanism of “dual-creative” talents in ethnic regions. The fuzzy hierarchical analysis-fuzzy comprehensive evaluation model consists of the evaluation system, a fuzzy comprehensive evaluation method, and a fuzzy hierarchical analysis. Then, based on the characteristics of fuzzy hierarchical analysis, the factors are organized and stratified to obtain a reasonable index set and build a hierarchical analysis structure. Using the fuzzy comprehensive evaluation method to analyze the school-enterprise cooperation in the integration of industry education in Tibetan higher vocational colleges, the results show that 55.4% of the students chose extremely unsatisfactory and unsatisfactory, indicating that they are not satisfied with the current communication status between the school and the internship enterprises, and another 28.6% of the students chose uncertainty full or unsatisfactory. This study explores the causes of the problems in the integration of industry-education personnel training from four levels: government, enterprises, society, and schools, and proposes guiding reference values for the integration of industry-education personnel training in Tibetan higher education institutions.

Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning

Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the capabilities of UAV platforms, we harnessed their efficiency in capturing multi-source, high-resolution remote sensing data. Our study focused on estimating potato LAI utilizing UAV-based digital red–green–blue (RGB) images, Light Detection and Ranging (LiDAR) points, and hyperspectral images (HSI). From these data sources, we computed four sets of indices and employed them as inputs for four different machine-learning regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), Histogram-based Gradient Boosting Regression Tree (HGBR), and Partial Least-Squares Regression (PLSR). We assessed the accuracy of individual features as well as various combinations of feature levels. Among the three sensors, HSI exhibited the most promising results due to its rich spectral information, surpassing the performance of LiDAR and RGB. Notably, the fusion of multiple features outperformed any single component, with the combination of all features of all sensors achieving the highest R2 value of 0.782. HSI, especially when utilized in calculating vegetation indices, emerged as the most critical feature in the combination experiments. LiDAR played a relatively smaller role in potato LAI estimation compared to HSI and RGB. Additionally, we discovered that the RFR excelled at effectively integrating features.

An Evaluation Model for the Influence of KOLs in Short Video Advertising Based on Uncertainty Theory

In the era of rapid growth in the short video industry, it is very important to find more accurate suitable advertising promoters, namely Key Opinion Leaders, to promote the development of short video commerce. A mathematical method is needed to grade and evaluate KOL’s abilities. Only in this way can advertisers better determine the value of KOL and determine whether it is suitable for promoting its products. Moreover, in the hierarchical evaluation of KOL, there is not only structured and quantifiable information, but also a large amount of unstructured and linguistic non-quantifiable information. Therefore, this article regards unquantifiable information as an uncertain variable and uses a comprehensive evaluation method based on uncertainty theory to handle subjective uncertainty in the evaluation process. Among them, all uncertain variables are symmetric. The main contribution of this article is the provision of a new evaluation method for KOL grading. Firstly, a two-level evaluation index system for KOL was established. Secondly, the importance and annotation of the Index set are set as uncertain variables, and the KOL evaluation model is constructed. Finally, two KOLs on TikTok were selected for comparative analysis to determine the importance ranking and KOL scores of each level of indicator, verifying the effectiveness and practicality of this method.

Morse index, Betti numbers, and singular set of bounded area minimal hypersurfaces

Morse index, Betti numbers, and singular set of bounded area minimal hypersurfaces

Burned area detection and mapping using time series Sentinel-2 multispectral images

Accurate mapping of burned areas (BAs) is essential for post-disaster reconstruction, air quality assessment, and estimation of fire emissions. Remote sensing has become the most practical means to map global/regional BAs. Space-borne coarse-resolution (250–1000 m) BA products play an essential role in fire-related science and applications, but small BAs are typically poorly represented due to resolution limitations. Moderate-resolution (10–30 m) imagery provides a more detailed spatial extent of BAs, but the various disturbances (e.g., plant phenology, cloud shading, flooding, harvesting, grazing, pests, and diseases) make application on a global scale challenging. Here, we propose an automatic BA detection algorithm based on time series images acquired by the Sentinel-2 MultiSpectral Instrument (MSI) and the Google Earth Engine. Specifically: (i) We determined an optimal set of spectral indices for MSI images based on global sampling to mitigate the background heterogeneity and BA spectral diversity. (ii) We jointly used three temporal scales, i.e., the single-temporal, dual-temporal, and short-time series MSI images, to identify potential BA regions (i.e., BA candidates). (iii) We integrated multi-source active fire (AF) products derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Operational Land Imager (OLI), and MSI instead of single-source products to refine BA candidates. (iv) We constructed an interannual time series to improve the BA candidates not confirmed by AF products. The proposed BA detection method was applied to different land cover and regional scales, including forests, grasslands, savannas, shrublands, and croplands at the regional (scene) and national levels (Portugal). Validation and inter-comparison with similar products demonstrated that the proposed method is reliable: the Dice coefficient of our detection results was improved by ∼7% and ∼ 9%, respectively, compared to the moderate-resolution BA products (e.g., LBA_CU for the conterminous United States and FireCCISFD20 for sub-Saharan Africa in 2019). These results suggest that the proposed method can accurately and automatically map BAs, providing a foundation for fire monitoring programs and climate change research.

Generalised Income Inequality Index

SummaryThis paper proposes a deep generalisation for income inequality indices. A generalised income inequality index that depends on two parameters and that involves a large set of income inequality indices in the same framework is proposed. The two parameters control the sensitivity of the generalised index to different levels of the income distribution. A thorough investigation of the generalised index paves the way for understanding the influence of the low, middle and high incomes on various income inequality indices and thereby facilitates the choice of multiple indices simultaneously for a better analysis of inequality as advocated by several recent studies. Moreover, two methods for estimating the generalised index in the case of finite populations are shown. A new method for estimating the inequality indices is proposed.

Tonal partition algebras: fundamental and geometrical aspects of representation theory

For we define tonal partition algebra over . We construct modules for over , and hence over any integral domain containing (such as ), that pass to a complete set of irreducible modules over the field of fractions. We show that is semisimple there. That is, we construct for the tonal partition algebras a modular system in the sense of Brauer. Using a “geometrical” index set for the Δ-modules, we give an order with respect to which the decomposition matrix over (with ) is upper-unitriangular. We establish several crucial properties of the Δ-modules. These include a tower property, with respect to n, in the sense of Green and Cox-Martin-Parker-Xi; contravariant forms with respect to a natural involutive antiautomorphism; a highest weight category property; and branching rules.

Research on harmonic resonant fault mechanism and suppression measures of oilfield power grid based on fast mode analysis method

In recent years, with the use of electric drilling rig, electric fracturing pump, and other equipment, the harmonic resonance problem of oilfield power grid is becoming more and more serious. In view of the high time and space complexity and long calculation time of the traditional harmonic resonance analysis method, the improved power iteration method is combined with the traditional modal analysis method, and the harmonic resonance problem of the oilfield power grid is analyzed by the fast modal analysis method. At the same time, a new set of harmonic resonance amplification index is derived and verified, and the harmonic content amplification times in the resonant influence region and different busbars are revealed. And then a passive filter is used to suppress harmonic resonance, and the effectiveness of the scheme is verified by field tests. The results show that, in the frequency range of 0–1000 Hz, the resonant frequency points in the oil field are mainly related to the rectifier pulse number of the drilling rig and the frequency conversion device for fracturing. The proposed fast modal analysis method has the advantages of fast convergence speed and memory saving, which is helpful to accurately detect the harmonic resonance region caused by the multi-pulse frequency conversion device, and the harmonic resonance can be effectively suppressed by installing a filter at the main excitation bus of the resonant frequency.

汽车金融公司多维盈利能力分析工具的应用探索

This article combines the theoretical research and practice of domestic and foreign experts on automotive finance, and uses principal component analysis to select ROE, ROA, and RCRA to construct a comprehensive index representing the company's profitability. Based on the four dimensions of the balanced scorecard, this paper analyzes the possible factors that affect the efficiency index of auto financing companies, and then uses the Delphi method method to obtain the final Index set. Combining the industry situation of auto financing companies, this paper selects the influencing factors. On the basis of the existing research results, this paper designs a model for the correlation between profitability and enterprise growth, non-performing loan ratio, and empirically studies all 25 domestic auto financing companies from 2011 to 2020, Analyze the relationship between various factors that affect profitability, and finally propose reasonable suggestions based on research conclusions and the actual situation of China's automotive finance industry.

Poor performance of acoustic indices as proxies for bird diversity in a fragmented Amazonian landscape

Biodiversity loss is rampant worldwide, particularly in tropical regions like the Amazon. In the last decade, acoustic indices have been proposed as a rapid method for biodiversity assessment. However, their overall effectiveness as proxies for biodiversity is under debate. Here, we tested advanced statistical methods based on acoustic indices recently proposed to estimate species richness more accurately. Using an annotated audio dataset (2356 one-minute files) and song characterization of tropical bird assemblages from land-bridge islands in an Amazonian hydroelectric reservoir, we fitted both regression models and random forest algorithms to address the following questions: (1) do acoustic indices provide accurate estimates of bird species richness? (2) are univariate (a single index) or multivariate models (a combination of indices) better at predicting species richness? (3) at what temporal scale (minutes or hours) and with which measures (raw values or mean and standard deviation values)? (4) do these indices reflect spatial (island size) and temporal patterns (diel cycle) of singing activity? Although we found that multivariate models using a set of acoustic indices computed at a broader scale (hours) performed better than simpler models, their overall predictive power of species richness was poor for these tropical bird assemblages. The high heterogeneity and variation in the acoustic activity and signals of the Amazonian bird species present a considerable challenge for acoustic indices to capture changes in species diversity adequately. In agreement with recent studies, our findings point out the limits of acoustic indices, especially in tropical, highly diverse regions, emphasizing that caution should be used when applying this type of acoustic indices in biodiversity assessment. In contrast, all tested indices reflected distinct spatial and temporal patterns that were often related to habitat features (i.e. island size) and animal activity (i.e. choruses), supporting alternative (large-scale) applications of acoustic indices. Random forest algorithms confirmed the potential to classify island size based on soundscape characteristics. These findings suggest acoustic indices can capture differences in assemblage composition and bird activity along a habitat fragmentation gradient. Hence, they can more efficiently assess habitat and community structure than species diversity, occurrence, or abundance.

A weather-based power distribution system reliability assessment

The continuity of power supply is affected by many factors; the weather conditions to which power system components are exposed are considered the most effect. A new methodology to assess the reliability of distribution system considering the impact of different weather conditions is presented in this paper. The proposed method considers possible future changes in the weather patterns that might occur differently from what it was in the past. Modified sets of reliability indices are presented for mathematically predicting reliability performance based on historical reliability data under specific past weather conditions combined with forecasted future weather conditions. The proposed method is applied to the distribution system components of RBTS; the results demonstrate the significance of considering the forecast of weather conditions in reliability assessment.