Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation
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This study found that increasing training data for LSTM networks improved runoff simulation accuracy and stability while reducing overfitting, using generated rainfall and runoff data from KNN and SWAT models.
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This study evaluates how the amount of training data affects the learning performance of an LSTM network for runoff simulation. Using runoff time series generated for 130 years via a combination of K-nearest neighbors (for meteorological series based on observed data) and the SWAT model (to produce runoff), the authors trained LSTM models with rainfall and runoff training windows of 5, 10, 20, 40, and 80 years and used a 50-year period for validation across the Yalong, Minjiang, and Jialing Rivers. The reported findings are that increasing training data reduces overfitting and improves prediction accuracy and stability. A major limitation stated implicitly by the design is that the runoff series are not directly observed but are model-generated, which may constrain generalizability. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract With the rapid development of Artificial Intelligence (AI) technology, an increasing number of intelligent algorithms have been used for simulating and forecasting hydrological process, among which the Long Short-Term Memory (LSTM) network is widely studied. The training of artificial intelligence networks often entails a large amount of training data, which contradicts the limitation of hydrological data. In this study, the effect of training data amount on the performance of LSTM network for runoff simulation are evaluated. First, the runoff series of 130 years are randomly generated by K-Nearest Neighbour (KNN) algorithm and SWAT model. The K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series based on the observed data, and the SWAT model is used to obtain the runoff series with the generated meteorological data series. Then, the LSTM models are developed and evaluated, with the 5-year, 10-year, 20-year, 40-year and 80-year data series of rainfall and runoff as training data respectively, and the 50-year data serves as validating data. The results obtained in Yalong River, Minjiang River and Jialing River show that (1) increasing the training data amount can effectively reduce the over-fittings of LSTM network; (2) increasing the training data amount can also improve the prediction accuracy and stability of LSTM network.
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Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation | 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 Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation Anbang PENG, Yuanyang TIAN, Wei XU, Xiaoli ZHANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1252947/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 With the rapid development of Artificial Intelligence (AI) technology, an increasing number of intelligent algorithms have been used for simulating and forecasting hydrological process, among which the Long Short-Term Memory (LSTM) network is widely studied. The training of artificial intelligence networks often entails a large amount of training data, which contradicts the limitation of hydrological data. In this study, the effect of training data amount on the performance of LSTM network for runoff simulation are evaluated. First, the runoff series of 130 years are randomly generated by K-Nearest Neighbour (KNN) algorithm and SWAT model. The K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series based on the observed data, and the SWAT model is used to obtain the runoff series with the generated meteorological data series. Then, the LSTM models are developed and evaluated, with the 5-year, 10-year, 20-year, 40-year and 80-year data series of rainfall and runoff as training data respectively, and the 50-year data serves as validating data. The results obtained in Yalong River, Minjiang River and Jialing River show that (1) increasing the training data amount can effectively reduce the over-fittings of LSTM network; (2) increasing the training data amount can also improve the prediction accuracy and stability of LSTM network. LSTM Rainfall runoff Data amount Over-fitting Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 02 Mar, 2022 Reviews received at journal 25 Jan, 2022 Reviewers invited by journal 25 Jan, 2022 Editor assigned by journal 13 Jan, 2022 First submitted to journal 11 Jan, 2022 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. 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