Ijaz Ul Haq

Quality control of streamflow observations is a critical bottleneck in hydrological monitoring, where manual expert review cannot scale with expanding sensor networks. We introduce HydroGEM (Hydrological Generalizable Encoder for Monitoring), a foundation model for continental-scale streamflow quality control. HydroGEM uses a two-stage approach: self-supervised pretraining on 6.03 million clean sequences from 3,724 USGS stations, followed by fine-tuning with synthetic anomalies for detection and reconstruction. A hybrid TCN-Transformer architecture (14.2M parameters) with hierarchical normalization enables learning across six orders of magnitude in discharge. On 799 held-out USGS stations with 18 anomaly types, HydroGEM achieves F1 = 0.792 and 68.7% error reduction, outperforming the strongest baseline by 2.0×. Zero-shot transfer to 100 Canadian ECCC stations yields Tolerant F1 = 0.70 with 90.1% segment detection. A human-in-the-loop design supports operational quality control workflows.

Streamflow and Stage Data-ECCC (1400 sites)

Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually, particularly in tropical and subtropical regions. This study proposes an advanced automated approach for malaria detection by classifying red blood cell images using machine learning and deep learning techniques. Three distinct models: Logistic Regression (LR), Support Vector Machine (SVM), and Inception-V3 were implemented and rigorously evaluated on a dataset comprising 27,558 cell images. The LR model achieved an accuracy of 65.38%, while SVM demonstrated improved classification performance with an accuracy of 84%. The deep learning-based Inception-V3 model outperformed both, achieving a classification accuracy of 94.52% after five training epochs, demonstrating its superior capability to extract intricate features from medical images. These results highlight the effectiveness of deep learning architectures in malaria diagnosis and pave the way for scalable, automated solutions, particularly in resource-limited settings.

Social media platforms often assume that users can self-correct against misinformation. However, social media users are not equally susceptible to all misinformation as their biases influence what types of misinformation might thrive and who might be at risk. We call “diverse misinformation” the complex relationships between human biases and demographics represented in misinformation. To investigate how users’ biases impact their susceptibility and their ability to correct each other, we analyze classification of deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: (1) their classification as misinformation is more objective; (2) we can control the demographics of the personas presented; (3) deepfakes are a real-world concern with associated harms that must be better understood. Our paper presents an observational survey (N = 2016) where participants are exposed to videos and asked questions about their attributes, not knowing some might be deepfakes. Our analysis investigates the extent to which different users are duped and which perceived demographics of deepfake personas tend to mislead. We find that accuracy varies by demographics, and participants are generally better at classifying videos that match them. We extrapolate from these results to understand the potential population-level impacts of these biases using a mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that diverse contacts might provide “herd correction” where friends can protect each other. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.

The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. This paper introduces TransNAS-TSAD, a framework that synergizes the transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This approach effectively tackles the complexities of time series data, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models due to its tailored architectural adaptability and the efficient exploration of complex search spaces, leading to marked improvements in diverse data scenarios. We also introduce the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the balance between accuracy and computational resources. TransNAS-TSAD sets a new benchmark in time series anomaly detection, offering a versatile, efficient solution for complex real-world applications. This research highlights the TransNAS-TSAD potential across a wide range of industry applications and paves the way for future developments in the field.

Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO₃, K, Mg, Na, total dissolved N, total organic N, NO₃, dissolved oxygen, pH (field and lab), Si, SO₄, and water temperature. Annual deposition loads and concentrations include hydrogen, NH₄, NO₃, total inorganic N, Cl, SO₄, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.