Papers by christian mulomba
IEEE Access, 2025
Traditional spatiotemporal data analysis often relies on predictive models that overlook causal r... more Traditional spatiotemporal data analysis often relies on predictive models that overlook causal relationships, making it difficult to identify true drivers and formulate effective interventions. To bridge this gap, we review causal machine learning (CML) techniques for spatiotemporal data, aiming to provide robust insights into their unique advantages. Our literature review reveals that fewer than 1% of studies in major databases explicitly integrate CML with spatiotemporal analysis. After rigorous screening, we analyze 51 relevant papers, categorizing their contributions into four key areas (totaling 62 methodological approaches due to multi-category papers): 1) causal effect discovery and estimation (32 approaches), 2) prediction accuracy enhancement (19), 3) pattern recognition limitations (10), and 4) interpretability (1). This distribution highlights a critical research gap, particularly in interpretability and comprehensive frameworks. We further examine unique challenges in spatiotemporal data, such as spatial autocorrelation and temporal dependencies, that complicate causal inference but also present opportunities for innovation. Promising approaches include the synergy of spatiotemporal Granger causality and structural equation modeling with spatial lags, which capture complex interdependencies while preserving interpretability. Future directions include developing interpretable causal models, advancing real-time causal inference in dynamic environments, and addressing computational challenges (scalability, efficiency, and complexityinterpretability trade-offs). We also discuss ethical considerations, such as bias mitigation in causal discovery and societal implications of spatiotemporal causal inference. By synthesizing challenges and opportunities, this work advances the application of CML in spatiotemporal analysis, with implications for climate science, economics, epidemiology, and urban planning. INDEX TERMS Causal machine learning, spatiotemporal data analysis, synergy methods, ethics.
Sustainability, 2024
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Review of Integrative Business and Economics Research, 2024
This research leverages a comprehensive global weather database to validate the potential of shor... more This research leverages a comprehensive global weather database to validate the potential of short-term air quality predictions using minimal data. This approach is particularly promising for resource-limited environments, which are often more vulnerable to such hazards compared to developed nations. The study employs machine learning methodologies and incorporates meteorological, air pollutant, and Air Quality Index (AQI) features from 197 capital cities. Our findings underscore the efficacy of the Random Forest algorithm in generating reliable predictions, especially when applied to classification rather than regression. This approach enhances the generalizability of the model on unseen data by 42%, considering a crossvalidation score of 0.38 for regression and 0.89 for classification. To instill confidence in these predictions, different methods for explainable machine learning were considered. This research highlights the potential for resource-limited countries to independently project shortterm air quality while waiting for larger datasets to enhance their predictions. Implementing this approach could promote public health and reduce dependence on external entities. In conclusion, this study serves as a guiding light, paving the way towards accessible and explainable air quality forecasting.
International Journal of Advanced Smart Convergence , 2023
The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs tha... more The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.
European Journal of Theoretical and Applied Science, 2024
Extreme events, despite their rarity, pose a significant threat due to their immense impact. Whil... more Extreme events, despite their rarity, pose a significant threat due to their immense impact. While machine learning has emerged as a game-changer for predicting these events, the crucial challenge lies in trusting these predictions. Existing studies primarily focus on improving accuracy, neglecting the crucial aspect of model explainability. This gap hinders the integration of these solutions into decision-making processes. Addressing this critical issue, this paper investigates the explainability of extreme event forecasting using a hybrid forecasting and classification approach. By focusing on two economic indicators, Business Confidence Index (BCI) and Consumer Confidence Index (CCI), the study aims to understand why and when extreme event predictions can be trusted, especially in the context of imbalanced classes (normal vs. extreme events). Machine learning models are comparatively analysed, exploring their explainability through dedicated tools. Additionally, various class balancing methods are assessed for their effectiveness. This combined approach delves into the factors influencing extreme event prediction accuracy, offering valuable insights for building trustworthy forecasting models.
International Journal of Advanced Culture Technology, 2023
Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy ... more Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shapewise feature engineering.
Journal of Intelligence and Information systems, 2024
The popularity of chaos analysis is attributable to the prevalence of chaotic systems across vari... more The popularity of chaos analysis is attributable to the prevalence of chaotic systems across various fields (Lahmiri & Bekiros, 2018), This prevalence has made their prediction a significant area of
Comparative Analysis of Deep Learning Algorithms in Forecasting Deterministic Chaos
SSRN Electronic Journal
Conference Presentations by christian mulomba
Hybrid Forecasting Classification Model for Anticipating Extreme Events in Industrial Production Total
한국지능정보시스템학회 학술대회논문집, 2023
The prediction of extreme events is a challenging task due to their infrequency and significant i... more The prediction of extreme events is a challenging task due to their infrequency and significant impact. Traditional methods may not be capable of capturing complex patterns in the data, prompting the proposition of a hybrid machine learning model in this study. The proposed model employs CNN, LSTM, and XGBoost algorithms to predict extreme events in the industrial production total index, an economic indicator that provides crucial information on the manufacturing, mining, and utility sectors' output and the overall health of the economy. Relevant data, including production levels and market demand, are collected and preprocessed, and an ensemble method is employed to enhance the forecasting reliability and accuracy. Performance is evaluated using various metrics, including accuracy and precision, and compared with those of traditional statistical methods
Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings
Conference on Interdisciplinary Business and Economics Research, 2024
Air pollution stands as the fourth leading cause of death globally. While extensive research has ... more Air pollution stands as the fourth leading cause of death globally. While extensive research has been conducted in this domain, most approaches rely on large datasets when it comes to prediction. This limits their applicability in low-resource settings though more vulnerable. This study addresses this gap by proposing a novel machine learning approach for accurate air quality prediction using two months of air quality data. By leveraging the World Weather Repository, the meteorological, air pollutant, and Air Quality Index features from 197 capital cities were considered to predict air quality for the next day. The evaluation of several machine learning models demonstrates the effectiveness of the Random Forest algorithm in generating reliable predictions, particularly when applied to classification rather than regression, approach which enhances the model's generalizability by 42%, achieving a cross-validation score of 0.38 for regression and 0.89 for classification. To instill confidence in the predictions, interpretable machine learning was considered. Finally, a cost estimation comparing the implementation of this solution in high-resource and low-resource settings is presented including a tentative of technology licensing business model. This research highlights the potential for resource-limited countries to independently predict air quality while awaiting larger datasets to further refine their predictions
Temporal Analysis of World Disaster Risk: A Machine Learning Approach to Cluster Dynamics
IEEE, 2023
The evaluation of the impact of actions undertaken is essential in management. This paper assesse... more The evaluation of the impact of actions undertaken is essential in management. This paper assesses the impact of efforts considered to mitigate risk and create safe environments on a global scale. We measure this impact by looking at the probability of improvement over a specific short period of time. Using the World Risk Index, we conduct a temporal analysis of global disaster risk dynamics from 2011 to 2021. This temporal exploration through the lens of the World Risk Index provides insights into the complex dynamics of disaster risk. We found that, despite sustained efforts, the global landscape remains divided into two main clusters: high susceptibility and moderate susceptibility, regardless of geographical location. This clustering was achieved using a semi-supervised approach through the Label Spreading algorithm, with 98% accuracy. We also found that the prediction of clusters achieved through supervised learning on the period considered in this study (one, three, and five years) showed that the Logistic regression (almost 99% at each stage) performed better than other classifiers. This suggests that the current policies and mechanisms are not effective in helping countries move from a hazardous position to a safer one during the period considered. In fact, statistical projections using a scenario analysis indicate that there is only a 1% chance of such a shift occurring within a five-year timeframe. This sobering reality highlights the need for a paradigm shift. Traditional long-term disaster management strategies are not effective for countries that are highly vulnerable. Our findings indicate the need for an innovative approach that is tailored to the specific vulnerabilities of these nations. As the threat of vulnerability persists, our research calls for the development of new strategies that can effectively address the ongoing challenges of disaster risk management.
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Papers by christian mulomba
Conference Presentations by christian mulomba