The research, led by Moïse Musubao Kapiri of the University of Liège and the Université Catholique du Graben, together with colleagues in Belgium and the Democratic Republic of Congo, surveyed 249 households located in flood-prone areas of Butembo. Rather than simply asking residents whether they felt worried about flooding, the team built a formal statistical model of preparedness behavior, grounded in the Theory of Planned Behavior — a widely used psychological framework that explains actions through intention, attitudes and perceived control over one’s own behavior. To this core model they added four further cognitive and non-cognitive factors: flood risk perception, direct experience of flooding, indirect experience gained through traditional and social media, and the degree of community involvement.

The methodological engine of the study is covariance-based structural equation modeling, or CB-SEM, a statistical technique that allows researchers to test networks of cause-and-effect relationships between concepts that cannot be observed directly, such as intention, perceived control or risk perception. Instead of measuring preparedness as a single yes-or-no question, the approach models it as a latent variable inferred from multiple indicators, while estimating the strength of the pathways linking each psychological and social factor to the outcome. This makes it possible to ask not only whether a factor matters, but how much it matters relative to the others — a crucial distinction when policy budgets are limited.

The baseline results are instructive in their own right. The classic Theory of Planned Behavior factors alone accounted for 45.8 percent of the variance in household preparedness. In other words, whether residents intended to prepare, and whether they believed they had the means to do so, explained nearly half of the differences between households. When the researchers added flood risk perception, direct and media-mediated experience, and community involvement, the model’s explanatory power jumped to 68 percent — a substantial gain that signals how much of preparedness behavior lies outside the standard psychological playbook.

Within that expanded model, the strongest single driver of preparedness was direct experience of flooding, with a standardized coefficient of 0.51. Households that had lived through a flood were markedly more likely to have taken protective measures than those that had not. Indirect experience — encountering flood events through traditional and social media — also had a significant positive effect, at 0.19, suggesting that information channels do shape behavior even for people who have not been flooded themselves. Perceived behavioral control, which in this context includes access to financial and material resources as well as technical knowledge, contributed a coefficient of 0.15, and the intention to prepare added a smaller but still significant 0.05. All of these effects were statistically significant at the p < 0.05 level.

The most striking finding, however, is what did not predict preparedness. Flood risk perception — the degree to which residents felt their homes and families were threatened — showed no statistically significant effect on household preparedness, with a coefficient of 0.05 and a p-value above the conventional threshold for significance. The authors describe this as a paradox of flood risk perception in this southern city: a community can collectively feel a heightened sense of danger, yet that shared anxiety does not translate into individuals actually stocking supplies, reinforcing structures or drawing up evacuation plans. The finding echoes a broader debate in the disaster literature, sometimes called the risk perception paradox, in which high awareness coexists with low protective action.

Why might feeling at risk fail to produce action? The study’s structure offers clues. If perceived behavioral control — money, materials and technical know-how — is a significant pathway to preparedness, then residents who recognize the danger but lack the resources to act may rationally remain unprepared, however acute their worry. Fear, in other words, is not a substitute for capacity. This interpretation aligns with the study’s emphasis that preparedness is not merely a matter of information deficits but of enabling conditions, and it helps explain why awareness campaigns alone so often disappoint disaster managers in cities across the Global South.

The role of experience, both direct and mediated, points to a second practical lever. Because direct flood experience was by far the strongest predictor, programs that connect at-risk households with the lived experiences of flooded communities — through testimonials, local radio reporting or social media storytelling — may do more to spur action than abstract warnings. The significant effect of media-based experience also underscores the importance of clear, reliable information dissemination: in a city where many residents encounter floods secondhand, the quality and credibility of what circulates on airwaves and social platforms can shape real preparedness behavior.

The context makes these findings urgent. Cities of the Global South are expanding rapidly while climate change intensifies rainfall extremes, and uncontrolled urbanization pushes settlements onto vulnerable land. Butembo, like other cities in the Kivu region of eastern DR Congo, faces exactly this combination of hydrological hazard, demographic pressure and strained infrastructure. The study’s authors argue that flood risk management in such cities must move beyond awareness-raising toward integrating risk perception into public policy and promoting strong community engagement in preparedness activities — treating residents not as passive audiences for warnings but as participants in designing and sustaining protective measures.

For researchers, the study is also a demonstration of method. By extending the Theory of Planned Behavior with cognitive and non-cognitive factors and testing the full structure with covariance-based structural equation modeling, the team quantified precisely how much each ingredient contributes — and showed that the standard recipe leaves nearly a third of the story untold. For practitioners, the message is sharper still: in Butembo, the households most likely to be ready for the next flood are those that have been flooded before, those that hear credible accounts through the media, and those with the financial means, materials and technical knowledge to act. Worry alone, however widespread, will not build a resilient city.

Subject of Research: Cognitive and non-cognitive determinants of household flood preparedness in Butembo, eastern DR Congo

Article Title: Assessment of cognitive and non-cognitive factors in household flood preparedness in the city of Butembo (eastern DR Congo)

Article References: Kapiri, M. M., Kavingwa, E. K., Mathe, I. K., Mahamba, J. A., Sahani, W. M., de Longueville, F., & Ozer, P. (2026). Assessment of cognitive and non-cognitive factors in household flood preparedness in the city of Butembo (eastern DR Congo). SN Social Sciences, 6(10), Article 519. https://doi.org/10.1007/s43545-026-01819-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01819-5

Keywords: flood preparedness, risk perception, Butembo, DR Congo, Theory of Planned Behavior, structural equation modeling, household behavior, disaster risk management, media experience, community involvement, urbanization, climate change

The core idea behind the work is post-processing. Rather than replacing numerical weather prediction altogether, the researchers treat the model output as a starting point that can be refined by a neural network trained on observations. This approach acknowledges a practical reality: numerical models encode decades of atmospheric science and remain the backbone of operational forecasting, but their raw precipitation fields often need correction before they reach emergency managers, hydrologists and the public. Previous deep learning post-processing efforts have shown promise, but the authors identify two persistent weaknesses. First, many networks fail to capture meteorological features that operate across multiple spatial scales, from broad synoptic rain bands down to localized convective cells. Second, and perhaps more fundamentally, extreme precipitation is vanishingly rare in training data, creating a severe class imbalance that causes standard networks to systematically underpredict the very events that matter most for disaster preparedness.

Rain-PostEFNet addresses these weaknesses through a carefully assembled architecture that borrows proven components from computer vision research. For feature extraction, the framework employs EfficientNet, a convolutional neural network family renowned for its efficient scaling of network depth, width and input resolution. On top of that sits ECA-Net, an efficient channel attention mechanism that adaptively reweights the importance of different feature channels, allowing the model to emphasize the meteorological signals most relevant to rainfall at each location and scale. The third pillar is FPN-Swin-Unet, which combines a feature pyramid network with a Swin transformer-based U-Net to refine features across multiple resolutions. The U-Net lineage traces back to biomedical image segmentation, where encoder-decoder structures proved adept at recovering fine spatial detail, and the Swin transformer adds hierarchical, window-based attention that captures long-range dependencies without prohibitive computational cost.

What distinguishes the framework from many predecessors, however, is its multi-task learning strategy. Instead of treating precipitation forecasting as a single regression problem, Rain-PostEFNet jointly optimizes two related tasks: classifying whether rain will fall in a given category, and regressing the actual precipitation amounts. The classification branch is trained with a weighted focal loss, a function specifically designed for imbalanced datasets. Focal loss down-weights the easy, abundant examples of light or no rain and focuses the network’s learning capacity on the rare, hard examples of intense precipitation. The regression branch uses the familiar mean squared error loss to sharpen the quantitative accuracy of predicted rainfall amounts. By training both objectives simultaneously, the network learns representations that serve detection and estimation at once, which the authors argue is key to improving predictive skill for rare heavy rain events.

Evaluating a precipitation forecasting model fairly requires standardized benchmarks, and the team turned to PostRainBench, a comprehensive benchmark dataset for post-processing numerical weather prediction that spans three climatologically distinct regions: China, Germany and Korea. This diversity matters, because rainfall regimes differ dramatically across these countries, from the monsoon-driven deluges of East Asia to the more moderate frontal systems of Central Europe. Performance was measured with three established verification metrics: the Critical Success Index, which captures the ratio of correct rain detections to all predicted and observed events; the Heidke Skill Score, which measures improvement over random chance; and overall accuracy. Together these metrics reward models that find the balance between detecting real rain and avoiding false alarms, a trade-off that has long challenged forecast verification.

The results are remarkable, especially for the Chinese dataset. For moderate rainfall, Rain-PostEFNet achieved relative improvements in Critical Success Index of 63.94 percent in China, 4.28 percent in Germany and 2.1 percent in Korea compared with state-of-the-art baseline methods. For heavy rainfall, the gains were even larger in China, reaching 78.34 percent, alongside 9.09 percent in Germany and 7.68 percent in Korea. The Heidke Skill Score told a similar story, rising by 52.18 percent in China and 2.95 percent in Germany for moderate rain, and by 52.4 percent in China and 6.78 percent in Germany for heavy rain. The outsized Chinese improvements likely reflect both the challenging nature of that region’s precipitation and the severity of the baseline errors there, but the consistent direction of improvement across all three countries suggests the framework captures something genuinely general about how to correct numerical forecasts.

To verify that the performance gains truly stem from the proposed design rather than sheer model size, the authors conducted an ablation study, systematically removing components and measuring the impact. The analysis confirmed that both the multi-task learning framework and the attention mechanisms contribute meaningfully to refining the numerical predictions. In other words, the joint classification-regression objective and the adaptive channel attention are not decorative additions; each plays a measurable role in the network’s ability to detect and quantify rainfall. This kind of component-level validation is increasingly expected in machine learning research, where headline numbers can sometimes mask architectures whose benefits are poorly understood.

The practical implications extend well beyond the leaderboard. Accurate precipitation forecasting underpins disaster preparedness, water resource management and climate modeling, and the events that current systems handle worst, extreme rainfall episodes, are precisely those with the highest human and economic stakes. A post-processing layer like Rain-PostEFNet could be deployed alongside existing operational numerical models, sharpening the rainfall fields that feed flood warning systems, reservoir operations and agricultural planning. Because the approach refines rather than replaces numerical forecasts, it complements the substantial investments meteorological agencies have already made in physical modeling, and it can be retrained as those models evolve. The authors also note the broader context of artificial intelligence entering earth system science, from neural global forecasting models to data-driven process understanding, positioning their work as part of a quiet revolution in how weather prediction is built.

Transparency was a priority for the team. The PostRainBench dataset is publicly available on GitHub, and the Rain-PostEFNet code has been released as well, allowing other researchers to reproduce the experiments, apply the framework to new regions and build on the architecture. The research was supported by the National Key R&D Program of China, the Joint Laboratory for Artificial Intelligence and Digital Meteorological Applications Research, the Yibin Science and Technology Program and the Sichuan Science and Technology Program, reflecting institutional commitment to integrating artificial intelligence into meteorological applications. The work was conducted by researchers at the University of Electronic Science and Technology of China, the Sichuan Artificial Intelligence Research Institute, the Institute of Plateau Meteorology of the China Meteorological Administration and the joint laboratory in Beijing.

Challenges remain before such systems become routine in operational forecasting. The severe class imbalance that motivates the focal loss never fully disappears, and performance gains in Germany and Korea, while consistent, are far more modest than in China, hinting that regional climate characteristics, dataset size and baseline quality all shape how much post-processing can deliver. Extreme events, by definition, offer few training examples, and no amount of architectural ingenuity fully substitutes for observations of rare phenomena. Still, the study demonstrates that multi-scale feature refinement and imbalance-aware training can extract substantially more skill from existing numerical forecasts, particularly for the heavy rainfall that matters most. As climate change intensifies the hydrological cycle and pushes precipitation extremes beyond the historical distributions on which both physical models and neural networks were trained, tools that squeeze additional accuracy from every forecast will only grow in value. Rain-PostEFNet offers a template for how computer vision and meteorology can converge on that task, one corrected rain map at a time.

Subject of Research: Deep learning post-processing of numerical weather prediction precipitation forecasts

Article Title: Enhancing NWP precipitation forecasting with Rain-PostEFNet: A multi-scale deep learning approach

Article References: Naz, F., She, L., Sui, X., Zhang, C., & Shao, J. (2026). Enhancing NWP precipitation forecasting with Rain-PostEFNet: A multi-scale deep learning approach. Applied Intelligence, 56(14), Article 408. https://doi.org/10.1007/s10489-026-07457-x

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07457-x

Keywords: precipitation forecasting, numerical weather prediction, deep learning, post-processing, extreme rainfall, multi-task learning, channel attention, EfficientNet, Swin transformer, PostRainBench, focal loss, flood preparedness