Urban inundation prediction using Seasonal Autoregressive Integrated Moving Average Model under extreme rainfall events

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Abstract

Abstract Urban inundation prediction enables the timely implementation of emergency measures and mitigates casualties and property losses caused by disasters. Physical models, hindered by their complex computational requirements, can no longer satisfy the demands for high-efficiency applications. In recent years, machine learning algorithms—particularly convolutional neural networks and recurrent neural networks have demonstrated substantial advancements in time series prediction tasks. This paper employs the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) to forecast inundation scenarios under a 100-year extreme rainstorm event in Jinan City, Shandong Province, China. Four water inundation points within the jurisdiction of the Yuxiu River Basin were selected as the prediction targets, and the inundation depth, unit flow rate, and flow velocity were predicted. The prediction accuracy for both long and short durations was analyzed. Additionally, it was discussed that variations in data volumes, rainfall durations, and rainfall peak coefficients had minimal impact on the prediction results. The results show that, the more available the data and the smaller the variation within the data during the time period, the higher the prediction accuracy. The RMSE values for inundation depth, unit flow rate, and node flow velocity predictions at the water accumulation points under the 100-year scenario were 0.016 m, 0.051 (m 3 /s), and 0.084 (m/s) respectively. These efforts are expected to contribute to the advancement of inundation prediction, offering a new perspective on rainfall events preprocessing to achieve more accurate predictions.
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Urban inundation prediction using Seasonal Autoregressive Integrated Moving Average Model under extreme rainfall events | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urban inundation prediction using Seasonal Autoregressive Integrated Moving Average Model under extreme rainfall events Xin Dang, Yongwei Gong, Qianting Chen, Xiaoxiao Lu, Kun Tian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8094046/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Urban inundation prediction enables the timely implementation of emergency measures and mitigates casualties and property losses caused by disasters. Physical models, hindered by their complex computational requirements, can no longer satisfy the demands for high-efficiency applications. In recent years, machine learning algorithms—particularly convolutional neural networks and recurrent neural networks have demonstrated substantial advancements in time series prediction tasks. This paper employs the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) to forecast inundation scenarios under a 100-year extreme rainstorm event in Jinan City, Shandong Province, China. Four water inundation points within the jurisdiction of the Yuxiu River Basin were selected as the prediction targets, and the inundation depth, unit flow rate, and flow velocity were predicted. The prediction accuracy for both long and short durations was analyzed. Additionally, it was discussed that variations in data volumes, rainfall durations, and rainfall peak coefficients had minimal impact on the prediction results. The results show that, the more available the data and the smaller the variation within the data during the time period, the higher the prediction accuracy. The RMSE values for inundation depth, unit flow rate, and node flow velocity predictions at the water accumulation points under the 100-year scenario were 0.016 m, 0.051 (m 3 /s), and 0.084 (m/s) respectively. These efforts are expected to contribute to the advancement of inundation prediction, offering a new perspective on rainfall events preprocessing to achieve more accurate predictions. Urban inundation Seasonal autoregressive integrated moving average Machine learning Rainfall duration Prediction method Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Dec, 2025 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 19 Dec, 2025 Editor assigned by journal 26 Nov, 2025 First submitted to journal 25 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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