{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T15:19:34Z","timestamp":1765898374619,"version":"3.48.0"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pennsylvania State University Office of Vice President for Commonwealth Campuses (OVPCC) Excellence in Interdisciplinary Research (EIR) Seed Funding Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Tornado occurrence and detection are well established in mesoscale meteorology, yet the application of deep learning (DL) to radar-based tornado detection remains nascent and under-validated. This study benchmarks DL approaches on TorNet, a curated dataset of full-resolution, polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar volumes. We evaluate three canonical architectures (e.g., CNN, VGG19, and Xception) under five optimizers and assess the effect of replacing conventional MLP heads with Kolmogorov\u2013Arnold Network (KAN) layers. To address severe class imbalance and label noise, we implement radar-aware preprocessing and augmentation, temporal splits, and recall-sensitive training. Models are compared using accuracy, precision, recall, and ROC-AUC. Results show that KAN-augmented variants generally converge faster and deliver higher rare-event sensitivity and discriminative power than their baselines, with Adam and RMSprop providing the most stable training and Lion showing architecture-dependent gains. We contribute (i) a reproducible baseline suite for TorNet, (ii) evidence on the conditions under which KAN integration improves tornado detection, and (iii) practical guidance on optimizer\u2013architecture choices for rare-event forecasting with weather radar.<\/jats:p>","DOI":"10.3390\/bdcc9120324","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:36:53Z","timestamp":1765895813000},"page":"324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KANs Layer Integration: Benchmarking Deep Learning Architectures for Tornado Prediction"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2390-243X","authenticated-orcid":false,"given":"Shuo (Luna)","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Business and Economics, Penn State Brandywine, 25 Yearsley Mill Road, Main 207D, Media, PA 19063, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehsaneh","family":"Vilataj","sequence":"additional","affiliation":[{"name":"School of Graduate Professional Studies, Penn State World Campus, University Park, PA 16803, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3256-1130","authenticated-orcid":false,"given":"Muhammad Faizan","family":"Raza","sequence":"additional","affiliation":[{"name":"Engineering Department, Penn State Great Valley, 30 E. Swedesford Rd., Malvern, PA 19355, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1377-3726","authenticated-orcid":false,"given":"Satish Mahadevan","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Engineering Department, Penn State Great Valley, 30 E. Swedesford Rd., Malvern, PA 19355, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","first-page":"e240006","article-title":"A benchmark dataset for tornado detection and prediction using full-resolution polarimetric weather radar data","volume":"4","author":"Veillette","year":"2025","journal-title":"Artif. Intell. Earth Syst."},{"key":"ref_2","unstructured":"National Weather Service (2023). Tornado Safety Guidelines and Impact Data."},{"key":"ref_3","unstructured":"National Weather Service (2023). 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