The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the inter-connectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environ-mental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause events such as mudslides and landslides. The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc., can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire or drought case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a "common-denominator-variable" in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). Insights from EIKG facilitate data analysis using both classical and Quantum Machine Learning (QML) techniques. The EIKG organizes heterogeneous datasets and integrates relationships to address extreme weather events. This study incorporates various datasets, including mobility data, socioeconomic data from the US Census Bureau, climate data from NASA, and critical infrastructure data.

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Global climate change is redefining our understanding of how diseases spread. In Sri Lanka, vector-borne diseases such as dengue fever historically surged during the monsoon seasons when temperatures were high enough for mosquito eggs to hatch. Unfortunately, due to rising temperatures and more erratic rainfall patterns, mosquito eggs can now hatch year-round making outbreaks increasingly unpredictable, leading to an alarming rise in hospitalizations and deaths. More data is needed to adapt our response to these diseases in an increasingly warmer world. In the contemporary landscape, a wealth of disease information is available, yet accessibility remains limited due to unstructured data formats such as PDFs. Therefore, converting unstructured disease reports into structured formats is necessary for effectively leveraging data. This paper introduces a comprehensive framework for collecting unstructured disease reports and transforming them into analyzable formats. By creating separate models tailored to each data format, we can ensure accuracy compared to general models. These straightforward models enhance accessibility and empower other researchers to use our tools. The returned structured data can then be harnessed for analysis, statistical purposes, and informing evidence-based public health interventions, thus facilitating more informed decision-making in healthcare. We deploy this framework to produce geospatial data for Sri Lanka and Brazil for many different conditions and align these data with satellite environmental data, providing for the first time a structured, aligned powerful dataset for disease modeling.

The global expansion of the dengue belt, driven by climate change and increased urbanization, has led to a significant rise in dengue cases worldwide (1). Early warning systems (EWS) coupled with prompt public health response mechanisms are crucial in mitigating dengue-related morbidity and mortality globally. In Sri Lanka, dengue transmission occurs year-round with two peaks correlating to the southwest monsoon from May to September and the northeast monsoon from October to January (2). The presence of multiple dengue virus serotypes (DENV1–4) complicates epidemiological patterns, as sequential infections with different serotypes can increase the risk of severe disease manifestations detected by surveillance systems (3). Understanding and integrating these virological dynamics, vector dynamics, and real-time surveillance data are essential for developing effective EWS and targeted public health interventions. We propose the use of Graph Neural Networks (GNNs) as an EWS. Using Earth observational data from NASA’s global satellites and dengue incidence data from Sri Lanka’s Ministry of Health, we developed traditional and graph-based EWS to forecast dengue cases across Sri Lanka’s 25 districts between 2013 and 2022. We demonstrate empirically that GNNs incorporating spatiotemporal relations significantly outperform traditional EWS models such as Autoregressive Integrated Moving Average (ARIMA), Random Forest, and Long Short-Term Memory (LSTM). Our source code is available on GitHub.