Trend Extrapolation Research Articles

Automobile-Demand Forecasting Based on Trend Extrapolation and Causality Analysis

Automobile-Demand Forecasting Based on Trend Extrapolation and Causality Analysis

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Estimation and Projection of Fertility in Nepal

The text discusses the importance of indirect estimation techniques in understanding population dynamics, particularly in countries with limited or flawed data like Nepal. Indirect methods offer valuable insights into fertility and mortality rates when direct measures are unavailable or unreliable. Techniques like Brass P/F ratio and the logistic curve function are explored for estimating fertility trends and projecting Total Fertility Rates (TFR). These methods involve meticulous analysis and adjustment for data inconsistencies. Indirect estimation plays a crucial role in bridging data gaps and informing demographic analysis and planning, especially in regions undergoing demographic transition. Through these techniques, researchers gain valuable insights into fertility dynamics, contributing to a better understanding of population trends. The text explores fertility estimation in Nepal using Arriaga's method, analyzing data from the 2011 and 2021 censuses. It reveals disparities in lifetime fertility indices, particularly after age 20, with higher rates among women aged 20-24 and 25-34, tapering towards the end of the reproductive span. Arriaga's adjustments address issues like misreporting of children's ages, aiming to rectify discrepancies and estimate the Total Fertility Rate (TFR) for both years. Additionally, the study discusses fertility projections for 2026 and 2031, highlighting the importance of trend extrapolation models and the logistic curve for accurate estimation. The findings underscore the significance of meticulous adjustments and reliable methodologies in understanding fertility dynamics in Nepal.

Estimation of Demand for Urban Land Uses: A Case Study of Türkiye

Land use change can have adverse impact on society and environment and therefore this puts enormous pressure on governments. Accurate estimates of future urban expansion are essential for sustainable growth and the preservation of the environment. This article examines the land use changes for urban uses, and further applies different methods for the projection of residential and industrial/commercial land uses in the selected case study area i.e. NUTS3 (nomenclature of terrestrial units for statistics) regions of Turkey. Density measures, trend extrapolation and regression analysis are the subject statistical methods used for projecting the land use. The findings show that using the chosen methodologies to project past changes leads in significant uncertainty. The results are significantly influenced by the variation in selected variables, and spatial organization of the study region. Therefore, validation analysis as a future research focus will be essential to select the most appropriate model that can be used to project the land use changes in Turkey. The results from the current analysis can be adopted by the government and local authorities for the land management and sustainable growth of urban land use in the Turkish regional context.

Reorientation of the Russian fuel and energy complex to new sales markets and changing export opportunities under sanctions pressure: challenges and prospects

The article is devoted to the study of Russia's interaction with key partners in the energy sector after a new round of sanctions pressure from the United States and Western countries. The article hypothesizes that the stability of the Russian fuel and energy sector after the introduction of restrictive measures is associated with the substitution of energy exports to eastern markets. The purpose of the work is to assess the prospects for the development of the fuel and energy complex of the Russian Federation under the conditions of sanctions pressure. The author examines such aspects of the topic as: changes in the volume and directions of exports of Russian energy resources, measures taken by Russian companies and the state to circumvent sanctions. Special attention is paid to the analysis of Russia's trade relations with China and India, including the dynamics of oil and gas exports, the impact of secondary sanctions on the business of these countries, and the adaptation of financial mechanisms to continue cooperation. The problems related to the trade balance and the use of national currencies in mutual settlements are also considered. In this paper, the author analyzes Russia's interaction with key partners in the fuel and energy sector through the paradigm of neorealism in the international relations theory. The methodological basis of this work is a problem-chronological scientific approach. The author also applied other general scientific research methods, including induction and deduction, as well as logical construction based on trend extrapolation. The author concluded that Russia has successfully reoriented energy exports to the markets of China and India after a new round of sanctions pressure in 2022. Secondary US sanctions put pressure on Chinese and Indian companies, in connection with which states refuse to conduct some transactions, but this does not lead to a significant decrease in energy supplies from Russia. The development of trade relations with China and India contributes to the preservation of Russian budget revenues from the oil and gas sector. A special contribution of the author is the analysis of adaptation strategies used by Russia, China and India to circumvent sanctions restrictions. The impact of sanctions on financial mechanisms and the trade balance between these countries is also assessed. At the same time, a comprehensive analysis of current and prospective export directions of Russian energy resources in the context of sanctions pressure has been carried out.

Population Aging in India: A Regional Comparison and Implications for Older Persons’ Welfare and Healthcare Infrastructure

Population aging is an inevitable consequence of demographic transitions, and declining fertility causes a rising burden of aging. The present study aims to project the aging index of various states nationwide up to 2031. In addition, the study estimates the year by which different states and union territories (UTs) will reach the Kerala levels index of aging (i.e., aged society). To conduct this study, data were gathered from the 2001 and 2011 Census of India. Using simple trend extrapolation techniques, we produced projections of aging indices up to 2031. Further, an index of aging of each state and union territory up to the level of Kerala state (i.e., aged society) was projected. The results showed that there will be a change in the age structure of the population from 2011 to 2031. There will be a 6% increase in the working-age group (15–59 years) and a 5% in the older population (60 years and above). However, there will be a decline of 11.2% in the children ages 0–14 years. The southern states and some states of north India, such as Punjab and Himachal Pradesh, are likely to attain Kerala’s level of aging index in the next few years. However, northern states are expected to take more years to achieve it. Therefore, there is a need to promote harmony between development and demographic change by expanding economic and social resources for older persons. The time has come to plan for the future burden of the older population across the country and arrange appropriate health facilities for them.

Development of an energy prediction model for residential buildings using Artificial Neural Network

A model has been developed in this study for predicting the energy consumption of residential building sector using Artificial Neural Network. This model was based on the Multi-Layer Perceptron architecture using feed-forward back propagation algorithm for training. The Levenberg-Marquardt function and the Gradient Descent with momentum function were used as training and learning function, respectively. The mean squared error was used to check the overall performance of the developed model. Trials were performed to finalize the number of hidden layers required for the model. Regression analysis was done between the predicted and actual data to validate the proposed ANN model. The prediction for the same dataset was also performed using the traditional trend extrapolation method, and the predicted results of both were compared with the actual energy consumption data recorded by the electricity regulatory authority of the state. It has been concluded that the accuracy of predicted data using the proposed model was very high (i.e., of 99.54%) as compared to the traditionally used TEM (i.e., 91.07%). MSE achieved for the ANN model was 0.01767% and that of TEM was 0.13115%. The outcome of this study can be used at building level to achieve energy efficiency by predicting the energy consumption and at the level of distribution companies to predict the overall energy demand by the respective sector, and take measures accordingly.

Application of Big Data and Artificial Intelligence in Substation Operation and Maintenance

In order to solve the problem that a large amount of equipment data obtained in the operation and maintenance work of substations is not fully utilized, this paper mainly analyzes and processes the data obtained by operators from the site by combining data mining technology. The first is to use the trend extrapolation method for data fitting to guide equipment maintenance; The second is to use multiple linear regression method to analyze the influencing factors of equipment status and carry out defect prediction and fault diagnosis; Third, by analyzing the predictability of DGA data, the prediction method based on the maximum Lyapunov index is used to predict the dissolved gas in the oil, so as to judge the operation status of the transformer and the occurrence of the fault through the analysis of DGA prediction data, and realize the prediction of the transformer fault. Reasonable use of big data technology and artificial intelligence technology can greatly improve the intelligent level of substation operation and maintenance work, thus optimizing the allocation of human resources, and making the work more accurate and efficient.

Modern Methods of Prediction

Humans have always wanted to know what the future holds in store for them. In earlier centuries, people often sought clues to the future from sacred texts. Today, more secular approaches are increasingly used, although the older approaches to the future persist. Modern methods for prediction include trend extrapolation, the Delphi method, mathematical modeling, and scenario analysis, including backcasting. Extrapolation was only possible when reliable past data became available. The Delphi method relies on the judgement of experts in the subject matter. Mathematical modeling has been very successful in the physical sciences, and, in the form of integrated assessment models (IAMs), has been applied to problems such as assessing future energy use. Scenario analysis looks at a number of possible futures and develops internally consistent story lines around each. It is often used in conjunction with IAMs. Each of the four methods, including both their strengths and weaknesses, are discussed in turn. Finally, this entry looks at the future of prediction, and concludes that despite progress in each of the four approaches treated, predicting the future, never easy, is now harder than ever.

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Previous machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP). We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.

Analysis of Demand and Structural Changes in China’s Water Resources over the Next 30 Years

The medium and long-term demand prediction for water resources is critical to the country’s macro-allocation of water resources and the formulation of a long-term regional economic development strategy. However, the official prediction report of China’s water resources was created ten years ago, and its results are far from accurate in practice making it difficult to direct China’s water resource planning in the coming ten years. By introducing the demand forecasting method for the first time, this study built a mathematical prediction model that integrates “data constraint + mathematical prediction + actuarial prediction”. The conclusions are as follows: (1) The new method developed in this research is more accurate than previous prediction methods and has a higher degree of matching with actual data. Despite the problem of trend extrapolation, this model can accurately understand the long-term trend. However, the amount of data must be calculated. (2) China’s consumption of total water, agricultural water, industrial water, domestic water, and ecological water will predict to be 552.9 billion m3, 319.1 billion m3, 95.7 billion m3, 95.5 billion m3 and 42.6 billion m3 in 2030, respectively, as well as 504.7 billion m3, 281.1 billion m3, 61.4 billion m3, 101.4 billion m3, and 60.8 billion m3 in 2050, respectively, indicating obvious structural changes. (3) China’s industrial structure evolves frequently; therefore, water resource forecasting must account for changes in industrial structure as well as geographical variances. In North China, agricultural water demand will fall, but ecological water investment will rise. The planning and distribution of the nation’s water resources will significantly be influenced theoretically and practically by the development of this methodology study. This method is universal and can be used for long-term prediction of future water demand in other countries.

Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods

Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has been applied to a vast range of technologies. A widely used approach in this field is trend extrapolation. Based on the literature available to us, there has been little or no attempt made to systematically review the empirical evidence on quantitative trend extrapolation techniques. This study attempts to close this gap by conducting a systematic review of the technology forecasting literature addressing the application of quantitative trend extrapolation techniques. We identified 25 studies relevant to the objective of this research and classified the techniques used in the studies into different categories, among which the growth curves and time series methods were shown to remain popular over the past decade while the newer methods, such as machine learning-based hybrid models, have emerged in recent years. As more effort and evidence are needed to determine if hybrid models are superior to traditional methods, we expect a growing trend in the development and application of hybrid models to technology forecasting.

Scenario modeling to predict changes in land use/cover using Land Change Modeler and InVEST model: a case study of Karaj Metropolis, Iran

Models for land cover/land use simulation are appropriate and important tools for decision-makers, helping them build future plausible landscape scenarios. Due to the fact that the simulation results of different models may be different, it is sometimes difficult for users to choose a suitable model. Therefore, in this study, an integrated approach is used, combining the data obtained from remote sensing and GIS with Land Change Modeler (LCM) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to simulate and predict land cover/land use changes for 2028 in Karaj metropolis (Northern Iran as a poor region-in terms of data-which is under intense and rapid urbanization. In this sense, three land cover/land use maps related to the study area were primarily generated using satellite image data for the period 2006, 2011, and 2017. They were used as a basis to define two scenarios: business-as-usual (BAU) scenario and participatory plausible scenario (PPS) for 2028. Afterwards, the necessary input data used in running of both models were prepared and, then, the outputs of the models were interpreted and compared. According to the results, while human-made coverage and low-density grasslands increased by about 74% and 12%, respectively, it was from 2006 to 2017 that agricultural lands, gardens, and high-density grasslands decreased by 42%, 34%, and 7%, respectively. According to the business-as-usual scenario, which was projected using the LCM model, the increase in human-made cover will continue by about 29% by 2028, and the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will experience decrease by about 20%, 3%, 11%, and 9%, respectively. The participatory plausible scenario for 2028, which was defined using the InVEST model, confirmed the same results, but having different quantities. Accordingly, while human-made cover will increase by about 73%, the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will decrease by about 41%, 10%, 16%, and 1%, respectively. The output quantities of InVEST scenario model seem to be closer to reality with less uncertainty, because this model estimates the quantity of demand for land and its suitability for different uses, based on the views of different stakeholders, and considers landscape development future policies and plans. In contrast, the LCM model is based solely on trend extrapolation from the past to current time and changes in the landscape structure.

A novel Mahalanobis distance method for predicting oil and gas resource spatial distribution

Accurate prediction of spatial distribution of petroleum resources is important for petroleum exploration. Mahalanobis distance is a popular and effective method to predict the spatial distribution of oil and gas. However, this method has equal weights for each variable, exaggerates secondary variables, and is prone to misjudge when the distance is close or equal, which impairs the accuracy of classification. To solve these problems, this paper proposes a novel Mahalanobis distance method based on genetic algorithm (GA-MD) to optimize attribute weights. The Sangonghe Formation in the hinterland of the Junggar Basin was used as an example, the validity of GA-MD was evaluated in the exploratory well data set. Compared with the current mainstream methods, the results show that the accuracy of GA-MD method is the highest, and the accuracy is improved by 2–6.2%. The application effect of the proposed method is verified by the prediction result of oil and gas probability map, the GA-MD method not only shows higher oil and gas bearing probability in the reserve areas but also has better trend extrapolation ability compared with other methods. Based on the GA-MD results, the favorable zones with remaining petroleum resources in the Sangonghe Formation in the hinterland of the Junggar Basin were visualized. Three types of favorable oil and gas distribution areas are selected. The favorable areas provide a basis for quantitative decision-making for the optimization of the next drilling strategy and determination of oil and gas exploration deployment direction in the study area.

Digital Hydraulic Design for Low-Specific-Speed Propeller Runners with Fixed Blades

The operating point of a propeller hydropower station will deviate from the effective workspace while the discharge reduces excessively during dry seasons. It usually leads to a decrease in efficiency and even to being unable to work. To solve the above problem, a scheme named decreasing capacity to increase efficiency was presented in this article. A low-specific-speed propeller runner with fixed blades that has the same installing dimensions as the original one was redesigned and equipped in dry seasons. A positive circulation at the outlet of the blades bigger than in conventional runners is allowed. Some key technologies about hydraulic design for runner blades were researched, which include distribution of velocity circulation at the inlet and outlet of the runner, thickening of the epiphyseal line of an aerofoil, unfolding aerofoil being converted to a cylindrical section, etc. In the section on digital modeling for runner blades, aerofoils on the cylindrical sections at the rim and at the hub were constructed employing the trend extrapolation method. Moreover, a blade digital model was built at one time according to the aerofoils on all cylindrical sections by means of a successful redevelopment to UniGraphics, and it has perfect symmetry. A case presented indicates that the method of decreasing capacity to increase efficiency is feasible. Using the method, the turbine efficiency increased from less than 28.6% to 83.4% while the discharge decreased from 3.20 m3s−1 to 1.00 m3s−1, and then the hydropower unit was able to work properly.

Prediction of China's Population Mortality under Limited Data.

Population mortality is an important step in quantifying the risk of longevity. China lacks data on population mortality, especially the elderly population. Therefore, this paper first uses spline fitting to supplement the missing data and then uses dynamic models to predict the species mortality of the Chinese population, including age extrapolation and trend extrapolation. Firstly, for age extrapolation, kannisto is used to expand the data of the high-age population. Secondly, the Lee-Carter single-factor model is used to predict gender and age mortality. This paper fills and smoothes the deficiencies of the original data to make up for the deficiencies of our population mortality data and improve the prediction accuracy of population mortality and life expectancy, while analyzing the impact of mortality improvement and providing a theoretical basis for policies to deal with the risk of longevity.

The effect of different prediction methods of population in villages and towns on assessing the suitability of biomass energy development

We investigate herein the development of biomass energy within a study area, to study how changes in population scale affects the suitability of biomass energy development in villages and towns. The analysis is performed by predicting the development trend of the total population and electricity consumption in the study area, and by establishing a biomass potential profit and loss model. With rural areas in western Guangdong as the research object, this study uses three different population prediction models, including the growth rate method, trend extrapolation method, and gray prediction method, to predict the population change trend of the study area in the next decade. The results of the growth rate method indicate a negative population growth rate of 5.28% for the study area over the next 10 years. We used Origin software to conduct polynomial fitting analysis on historical population data, and to make trend extrapolation predictions, the results demonstrate that in the short run, the population maintain a stable decline of approximately 1.7% by 2030. The results of the gray prediction model indicate that the population shows a slight decrease of 0.3‰.We determined the quantity of straw resources and the theoretical power generation potential of regional biomass based on the crop yields of Maoming, Zhanjiang, and Yangjiang, as well as the collectable coefficient and corresponding calorific value of straw in Guangdong Province. The total amount of crop straw resources in western Guangdong is approximately 120 522.6 tons, and the total biomass theoretical power generation potential is 305 × 103 MWh, the goal of 100% biomass energy supply will not be fully realized before 2030. Among these, the biomass energy supply in Maonan District of Maoming City is expected to reach 40–60%, and surplus energy demand may possibly combine with other renewable and traditional energy sources. The southern part of Zhanjiang has slightly less potential for biomass energy development than Maoming, with a maximum potential of 25%. All three prediction models suggest that Yangjiang is not suitable for biomass development in the short term, and its biomass potential will only meet 1% of its energy needs by 2030.

Can Norway become a net-zero economy under scenarios of tourism growth?

The Paris Agreement suggests that all countries engage in significant emission reductions. To stay within safe guardrails, usually defined as a maximum warming of 1.5 °C compared to pre-industrial times, this will mean decarbonisation within less than 30 years. This significant challenge is complicated because of growth in some sectors, such as tourism. This paper analyses emissions and economic output in Norway, considering national Tourism Satellite Accounts. Novel aspects of this paper include a longitudinal perspective covering 12 years through which critical developments and progress on emission reduction pathways can be assessed. Findings suggest that the carbon intensity of tourism (emissions per NOK) is more than twice the Norwegian economy average. Aviation in particular is a major barrier to emission reductions, as it generates 17% of national tourism revenue and 75% of direct tourism emissions. Trend extrapolation shows that tourism will be the largest emission sub-sector of the Norwegian economy by 2030. The Norwegian economy will have to decarbonise at a rate more than 30 times faster than its current rate, if it is to decarbonise to mid-century, while continuing on its observed economic growth trajectory.

Projections of urban built-up area expansion and urbanization sustainability in China's cities through 2030

In the context of socioeconomic initiatives to address world development sustainability such as the Sustainable Development Goals (SDGs), China's urbanization with increasing intensity and quickening pace has always been central to debates over the past few decades. Although the United Nations (UN) recommend to estimate and predict the local-scale urbanization sustainability by using the SDG indicator 11.3.1: “ratio of land consumption rate to population growth rate (LCRPGR)”, there remains a lack of reliable projections and applicable assessments at the city level, especially for the future urban expansion. Until now, limited knowledge of the urbanization sustainability dynamics in the next decade has been the primary challenge to inform the realization of urban sustainable development goal under the framework of SDGs. To evaluate the geographic region- and population size-specific urbanization sustainability at the city level in mainland China between 2020 and 2030, an integrated downscaling approach of trend extrapolation and regression analysis was developed based on historical statistics of urban built-up area and subnational scenarios of population and gross domestic product (GDP) under the Shared Socioeconomic Pathways (SSPs). The SSPs-consistent urban built-up area projections across different scales showed that the logistic regression had a better modeling performance than the panel data regression, while there was little difference between the linear and exponential extrapolation methods. Despite the geographical heterogeneity and size-related disparity, the scenario modeling results suggested that the average value of LCRPGR indicator in mainland China was expected to decline continuously, and would reach at the ideal state with a coordination between urban expansion and population growth through 2030. Specifically, the population growth stagnation of cities in Northeast China and the allometric urban expansion of cities in Western China should be the focus of strengthened regulations, and the urban growth of megacities would experience a steady and slower trend compared to their surrounding super-large cities and large cities. In summary, China's urbanization process might have headed toward a more sustainable and coordinated future under feasible SSPs scenarios. The local-scale forecasts will not only fill the data gap of SSPs-consistent urban land demand for all county- and prefecture-level cities in mainland China, but can also provide a reference for forecasting and assessment of urbanization sustainability to meet the SDGs in urban areas such as indicator 11.3.1.

Hybrid 4-Dimensional Trajectory Prediction Model, Based on the Reconstruction of Prediction Time Span for Aircraft en Route

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.