使用机器学习预测冲突事件,用于强迫移民模型
Yani Xue1, Thomas Schincariol2, Thomas Chadefaux2
1Department of Computer Science, Brunel University London, Uxbridge, UK. Yani.Xue3@brunel.ac.uk.
Scientific reports
|August 1, 2025
概括
准确预测冲突期间人口流离失所对于人道主义援助至关重要. 本研究介绍了一种混合模型,该模型结合了用于冲突预测的机器学习和用于移位的基于代理的建模,从而提高了预测准确性并减少了专家的努力.
科学领域:
- 计算社会科学 计算社会科学
- 地理空间分析的研究.
- 机器学习 机器学习
背景情况:
- 准确预测冲突期间人口流离失所的情况对于有效提供人道主义援助至关重要.
- 现有的冲突预测模型往往缺乏必要的空间和时间分辨率,以集成与流离失所模型.
- 针对流离失所预测的一般化建模方法需要准确的冲突动态估计,而这些估计很难获得.
研究的目的:
- 开发和验证一种混合方法,以提高冲突驱动人口流离失所预测的准确性.
- 将基于机器学习的冲突预测与基于代理的建模 (ABM) 整合起来,以改善预测.
- 减少对手工冲突估计和专家知识的依赖,以生成紧急流离失所预测.
主要方法:
- 一种混合方法,将冲突预测的随机森林分类器与人口移动的Flee ABM相结合.
- 结合模型验证使用马里,布隆迪,南苏丹和中非共和国的历史冲突案例研究.
- 利用机器学习来预测冲突动态以输入到基于代理的模型.
主要成果:
- 结合模型的预测准确度与传统方法预测人口流离失所的预测准确度相当.
- 该方法成功地将机器学习冲突预测与基于代理的建模集成在一起.
- 该方法减少了手动预先估计冲突的需求,简化了预测过程.
结论:
- 拟议的混合模型为预测因冲突而导致的人口流离失所提供了更准确,更有效的方法.
- 将机器学习与ABM集成,为人道主义应对计划提供了一个强大的框架.
- 这种方法降低了人道主义专业人员生成及时和可靠的流离失所预测的障碍.
相关概念视频
01:18Steps in Outbreak Investigation
204
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
204
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
00:53Migration
8.1K
Migration is long-range, seasonal movement from one region or habitat to another. This common strategy, carried out by many different organisms around the world, is an adaptive response that typically corresponds to changes in an organism’s environment, like resource availability or climate. Migrations can involve huge groups of thousands of animals as well as single individuals traveling alone and can range from thousands of kilometers to just a few hundred meters.
8.1K
01:11Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86