arXiv Machine Learning By Ankit Bhardwaj, Lakshminarayanan Subramanian

Identifiability-Aware Source Apportionment in City-Scale Advection-Diffusion Systems

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arXiv:2608. 00050v1 Announce Type: cross Abstract: Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variation, and noise.

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arXiv AI
Aug 11

AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting

arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.

By Fan Yang, Nan Chen, Yijie Dong, Yuchen Zhang, Wei Zhang
arXiv Machine Learning
Jun 25

Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications

arXiv:2606. 24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal, and physical-variable dimensions.

By Arindam Sengupta, Paul Jeanney, Ricardo Vinuesa, Jose Miguel Perez, Soledad Le Clainche
arXiv Machine Learning
Aug 7

Scalable estimation of VARMA models

arXiv:2608. 06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series.

By Daniel Paulin, Victor Elvira
arXiv AI
6d ago

Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: An Application and Evaluation of Data-Driven Sparse Sensing

The study evaluates a data‑driven sparse sensing (DSS) workflow for monitoring urban drainage networks, using a 77‑node model to optimize sensor placement and reconstruct flow conditions. By applying singular value decomposition, pivoted QR selection, and a reconstruction decoder to 225 simulated scenarios, a 3‑node layout (4 % of the network) achieved median Nash‑Sutcliffe efficiency of 0.791, with all cases exceeding 0.700. The method matched performance of Greedy D‑optimal and genetic algorithm approaches, proved robust to Gaussian noise, and identified that sensor loss sensitivity correlates with upstream drainage area and conduit characteristics.

By Zihang Ding, Amit Kumar, Imran Md. Azizul Islam, Mila Avellar Montezuma, Ruihang Zhang, Kun Zhang