Migration Forecasts Research Articles

Integrative models for environmental forecasting of phthalate migration from microplastics in aquaculture environments

The pervasive utilization of plastic tools in aquaculture introduces significant volumes of microplastic fibers, presenting a consequential risk through the leaching of additives such as phthalates. This study scrutinizes the leaching dynamics of six prevalent phthalate esters (PAEs) from thirteen plastic aquaculture tools comprising polyethylene terephthalate (PET), polypropylene (PP), and polyethylene (PE), with ΣPAEs ranging from 0.24 to 4.26 mg g−1. Di(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) emerged as predominant, marking significant environmental concern. Over a 30-day period, leaching quantities of Σ6PAEs from PET, PP, and PE fibers reached 36.65 μg g−1, 21.87 μg g−1 and 19.11 μg g−1, respectively, influenced by factors such as time, temperature, turbulence, and salinity. Notably, turbulence exerted the most pronounced effect, followed by temperature, with negligible influence from salinity. The kinetic models aligning with interface diffusion control was developed, predicting PAEs' leaching behavior with activation energies (Ea) indicative of the process's thermodynamic nature. The application of this model to real-world aquaculture waters forecasted significant risks, corroborating with empirical data and underscoring the pressing need for regulatory and mitigation strategies against PAEs contamination from aquaculture practices.

Forecasting migration movements using prediction markets

Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application.

Analysis and Prediction of Bird Migration Using RFA and GBR

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Abstract. Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.

Forecasting nocturnal bird migration for dynamic aeroconservation: The value of short‐term datasets

Abstract Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the ‘international first’ offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.

Nutritional Status, Sex, and Ambient Temperature Modulate the Wingbeat Frequency of the Diamondback Moth Plutella xylostella.

The diamondback moth, Plutella xylostella L. (Lepidoptera: Plutellidae), is a cosmopolitan horticultural pest that is undergoing a fast, climate-driven range expansion. Its wide geographic distribution, pest status, and high incidence of insecticide resistance are directly tied to long-distance migration. Wingbeat frequency (WBF) is a key aspect of P. xylostella migratory behavior, but has received limited scientific attention. Here, we investigated the effects of environmental parameters, age, adult nutrition, and sex on P. xylostella WBF. Across experimental regimes, WBF ranged from 31.39 Hz to 78.87 Hz. Over a 10-35 °C range, the WBF of both male and female moths increased with temperature up to 62.96 Hz. Though male WBF was unaffected by humidity, females exhibited the highest WBF at 15% relative humidity (RH). WBF was unaffected by adult age, but adult nutrition exerted important impacts. Specifically, the WBF of moths fed honey water (54.66 Hz) was higher than that of water-fed individuals (49.42 Hz). Lastly, males consistently exhibited a higher WBF than females. By uncovering the biological and (nutritional) ecological determinants of diamondback moth flight, our work provides invaluable guidance to radar-based monitoring, migration forecasting, and the targeted deployment of preventative mitigation tactics.

Advancing spatiotemporal forecasts of CO[formula omitted] plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO2 plume migration, achieving R2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. The methods proposed in our work enhances the performance and interpretability of CO2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

Algorithm for Automating Input and Processing of Initial Data for Computer Forecasting of Spatial Migration of Radionuclides in Soils

An important tool for solving problems associated with the migration of radionuclides is computer technology that makes it possible to predict the spread of radioactive contamination in soils, which are an important component of the biosphere. At the same time, most modern software for the prediction of the spread of radioactive contamination is based on idealizations that simplify the understanding of this process, while solving the problem of a comprehensive assessment of the biosphere requires the use of a full-fledged spatial model of radionuclide migration in soils. Such a model was developed by P. K. Shalkevich within SPS (Simulation of Processes in Soil) software. SPS uses the initial values of radionuclide concentrations, hydrological and thermal properties of soils and information about meteorological conditions as input data for forecasting. At the same time, the listed initial data must be previously collected, processed and converted into a form that the software able to work with. Such processes require significant intellectual and time costs. These costs negatively affect the possibility of generating operational forecasts of the radioactive situation, but can be significantly reduced by using systems for automated radiation monitoring of soils and automating input and processing of initial data when predicting the spatial migration of radionuclides in the appropriate software, what is considered in the present article.

FULL-STACK розробка інтелектуальної системи розвитку міграції населення

A generalised model of population migration is proposed. On the basis of the model of population migration, the article develops models of: a set of directions of population flows, which are formed on internal and external flows of the State; duration of migration, which is determined by its nature in time, including permanent or irreversible duration of migration, movement for a relatively short time, annual movement of people and pendulum duration of migration; type and form of migration. A model of indicators of actual migration (resettlement) that can characterise the overall level of mobility of the population of the territories, the scale, structure, directions and effectiveness of migration flows for a given period is developed and their groups are divided. It is proposed that the results of population migration should be presented in the form of a number of absolute and relative indicators for the purpose of further regression analysis of data, namely, those who arrived for permanent residence from other settlements; those who left for permanent residence to other settlements; migration balance or mechanical growth. Inter-rayon relations are characterised by the strength of migration flows. To obtain the results of migration, we take into account the strength of migration flows, which depend on the population of the territories between which the exchange takes place and on their location. The result of this exchange is expressed in the migration balance or by means of efficiency coefficients of migration ties. The intensity of migration exchange, independent of the population size of both the areas of origin and the places of settlement, is determined by the intensity coefficients of migration ties. The types of migration intensity coefficients are formed depending on the properties, namely the intensity coefficients of arrival (immigration), departure (emigration), reverse migration, and net migration. The intelligent geographic information system implements the lightgbm algorithm for population migration forecasting, which is a decision tree with gradient reinforcement. For data analysis, the migration forecasting system includes regression analysis and neural networks and is capable of predicting international migration or migration between different countries.

Wykorzystywanie systemów sztucznej inteligencji (SI) na zewnętrznych granicach Unii Europejskiej. Możliwości, zagrożenia i wyzwania

The European Union and its Member States are increasingly turning to artificial intelligence (AI) technologies to enhance border controls. This is one of the manifestations of a broader trend of moving towards the so-called smart borders of the Union. This article presents EU initiatives on the development and implementation AI-based digital technologies to improve the control and security of the Union’s external borders. The main directions of the use of AI were analyzed, i.e. in the field of automatic recognition of fingerprints and faces, emotion detection, algorithmic profiling and monitoring, analysis and forecasting of migration. The aim of the article is, on the one hand, to show that the careful implementation and use of AI-based technology for border control can bring many benefits. On the other hand, however, these benefits must be balanced with the significant threat posed by these technologies to the protection of the fundamental rights of the individual.

A deep learning-based direct forecasting of CO2 plume migration

Accurate and timely forecasts of CO2 plume evolution in geological reservoirs are crucial for CO2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO2 plume distributions. We demonstrate the effectiveness and accuracy of our method in predicting the CO2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

Parsimonious stochastic forecasting of international and internal migration on the NUTS-3 level – an outlook of regional depopulation trends in Germany

Substantiated knowledge of future demographic changes that is derived from sound statistical and mathematical methods is a crucial determinant of regional planning. Of the components of demographic developments, migration shapes regional demographics the most over the short term. However, despite its importance, existing approaches model future regional migration based on deterministic assumptions that do not sufficiently account for its highly probabilistic nature. In response to this shortcoming in the literature, our paper uses age- and gender-specific migration data for German NUTS-3 regions over the 1995–2019 period and compares the performance of a variety of forecasting models in backtests. Using the bestperforming model specification and drawing on Monte Carlo simulations, we present a stochastic forecast of regional migration dynamics across German regions until 2040 and analyze their role in regional depopulation. The results provide evidence that well-known age-specific migration patterns across the urban-rural continuum of regions, such as the education-induced migration of young adults, are very likely to persist, and to continue to shape future regional (de)population dynamics.

Ensemble predictions are essential for accurate bird migration forecasts for conservation and flight safety

Abstract Accurate predictions of the abundance of migrating birds are important to avoid aerial conflicts of birds, for example, with aviation or wind power installations. Here we develop a predictive model, using bird migration intensity extracted from operational weather data. We compare baseline phenological models to models incorporating both local and remote weather conditions using an ensemble approach. Single models are compared to ensemble models (average prediction of top 10 models). The models were evaluated by omitting single years from our 10‐year dataset. In general, we find that wind conditions, in addition to seasonal and diurnal dynamics, are key for accurate predictions. The spring and fall migratory seasons differ, both with respect to the selected environmental variables and the contribution of the environmental model compared to the phenological model. In fall, the accumulation of migrants due to strong headwinds is an important predictor of migration. Because of the lower daily variation in migration intensity in spring, the phenological model performs better compared to fall. In fall, weather conditions contribute more to accurate predictions of migration intensity than in spring. Overall, the ensemble approach produces more accurate predictions outperforming specific environmental models. We therefore recommend that ensemble models be used in operational settings such as flight planning to reduce bird aircraft collisions during intense bird migration.

To the question of migration of capital and labor force of Ukraine: forecast of future trends

This article aimed to identify the main prognostic scenarios of capital and labor migration of the Ukrainian state. Among the main prognostic models, most were formed and projected to 2030. Method. The descriptive method, method of analysis of quantitative indicators of emigration and financing of Ukraine, method of forecasting were used in the study. As for the forecasting of capital and labor migration, the basis was the analytical reports of the Club of Rome experts, who analyzed economic changes and demographic changes and predicted the probable consequences for the future. Results. The inevitable increase in labor emigration and reduction in the Ukrainian labor force were identified as the study’s main results. Conclusions. The main trends in the coming decades for the Ukrainians are the emergence of the global economic system, which will lead to competition in the labor market for the best and the best companies for workers. The basis of successful workers in the near future will be global thinking and skills.

Understanding the Climate-Conflict-Migration Nexus: Immigration from Climate-Conflict Zones to Turkey

Regardless of the level of development, all countries are affected by the negative consequences of climate change, and economic, environmental, and social consequences emerge accordingly. Climate change is now being considered as a development and security problem. The main reason for this is that climate-related extreme weather events cause disasters, and that climate change is a threat multiplier for conflicts as it increases the struggle for scarce natural resources. This paper attempts to explain migration to Turkey from Afghanistan, Pakistan, and Bangladesh based on climate-induced internal displacement data using the Foresight Model. The study focuses on whether it is possible to predict migration from these countries by using the Foresight Model based on the comparison of the limited data with the IDMC data. Immigration was examined in three countries, Afghanistan, Pakistan, and Bangladesh, with the exception of India, which was compatible with disaster-induced internal displacement IDMC data, by comparing DGMM data on illegal migrants apprehended with YÖK data on international students. It is concluded that a minimalist approach is more appropriate to explain climate induced migration to Turkey, and the micro and meso factors should also be considered based on the Foresight Model. IDMC, DGMM and YÖK data are compatible with each other, and there is a need for comprehensive data, including these data in climate-induced migration forecasts.

Forecasting asylum-related migration flows with machine learning and data at scale

The sudden and unexpected migration flows that reached Europe during the so-called ‘refugee crisis’ of 2015–2016 left governments unprepared, exposing significant shortcomings in the field of migration forecasting. Forecasting asylum-related migration is indeed problematic. Migration is a complex system, drivers are composite, measurement incorporates uncertainty, and most migration theories are either under-specified or hardly actionable. As a result, approaches to forecasting generally focus on specific migration flows, and the results are often inconsistent and difficult to generalise. Here we present an adaptive machine learning algorithm that integrates administrative statistics and non-traditional data sources at scale to effectively forecast asylum-related migration flows. We focus on asylum applications lodged in countries of the European Union (EU) by nationals of all countries of origin worldwide, but the same approach can be applied in any context provided adequate migration or asylum data are available. Uniquely, our approach (a) monitors drivers in countries of origin and destination to detect early onset change; (b) models individual country-to-country migration flows separately and on moving time windows; (c) estimates the effects of individual drivers, including lagged effects; (d) delivers forecasts of asylum applications up to four weeks ahead; (e) assesses how patterns of drivers shift over time to describe the functioning and change of migration systems. Our approach draws on migration theory and modelling, international protection, and data science to deliver what is, to our knowledge, the first comprehensive system for forecasting asylum applications based on adaptive models and data at scale. Importantly, this approach can be extended to forecast other social processes.

Now-casting Romanian migration into the United Kingdom by using Google Search engine data

Short-term forecasts of international migration are often based on data that are incomplete, biased, and reported with delays. There is also a scarcity of migration forecasts based on combined traditional and new forms of data.

Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg

This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.

ТАШҚИ МЕҲНАТ МИГРАЦИЯСИНИНГ ПРОГНОЗ КЎРСАТКИЧЛАРИ ВА УНИНГ УСЛУБИЙ АСОСЛАРИ

In this article, the development of the economy of Uzbekistan, migration movement of labor resources, causes, consequences of labor migration, problems, employment problems, migration forecasting indicators, negative and positive aspects of Population migration activity, additional proposals on migration accounting are made and methods of calculating migration are studied.The purpose of the study: to study the trends in the development of labor migration in the Republic of Uzbekistan, to make a clear and accurate interpretation and forecast of the omillarni that affects it.Scientific novelty of the study:1.A new procedure for obtaining information on vacancies, the nature of work, salaries, qualifications (skills)required from foreign employers is proposed;2.On the basis of the models of the forecast of migration to 2025 year, the dynamics of migration is shown, indicating the directions of its regulation

MicroRNA-21/PDCD4 Proapoptotic Signaling From Circulating CD34+ Cells to Vascular Endothelial Cells: A Potential Contributor to Adverse Cardiovascular Outcomes in Patients With Critical Limb Ischemia.

OBJECTIVEIn patients with type 2 diabetes (T2D) and critical limb ischemia (CLI), migration of circulating CD34+ cells predicted cardiovascular mortality at 18 months after revascularization. This study aimed to provide long-term validation and mechanistic understanding of the biomarker.RESEARCH DESIGN AND METHODSThe association between CD34+ cell migration and cardiovascular mortality was reassessed at 6 years after revascularization. In a new series of T2D-CLI and control subjects, immuno-sorted bone marrow CD34+ cells were profiled for miRNA expression and assessed for apoptosis and angiogenesis activity. The differentially regulated miRNA-21 and its proapoptotic target, PDCD4, were titrated to verify their contribution in transferring damaging signals from CD34+ cells to endothelial cells.RESULTSMultivariable regression analysis confirmed that CD34+ cell migration forecasts long-term cardiovascular mortality. CD34+ cells from T2D-CLI patients were more apoptotic and less proangiogenic than those from control subjects and featured miRNA-21 downregulation, modulation of several long noncoding RNAs acting as miRNA-21 sponges, and upregulation of the miRNA-21 proapoptotic target PDCD4. Silencing miR-21 in control CD34+ cells phenocopied the T2D-CLI cell behavior. In coculture, T2D-CLI CD34+ cells imprinted naive endothelial cells, increasing apoptosis, reducing network formation, and modulating the TUG1 sponge/miRNA-21/PDCD4 axis. Silencing PDCD4 or scavenging reactive oxygen species protected endothelial cells from the negative influence of T2D-CLI CD34+ cells.CONCLUSIONSMigration of CD34+ cells predicts long-term cardiovascular mortality in T2D-CLI patients. An altered paracrine signaling conveys antiangiogenic and proapoptotic features from CD34+ cells to the endothelium. This damaging interaction may increase the risk for life-threatening complications.