Baron Sam B, Isaac Sajan R, Chithra R. S, Manju C. Thayammal
{"title":"MAML-Enhanced LSTM for Air Quality Time Series Forecasting","authors":"Baron Sam B, Isaac Sajan R, Chithra R. S, Manju C. Thayammal","doi":"10.1007/s11270-024-07549-9","DOIUrl":null,"url":null,"abstract":"<div><p>Predicting air quality is essential for environmental monitoring and public health. In this work, we suggest a novel method for time series forecasting that uses Long Short-Term Memory (LSTM) networks and the Model-Agnostic Meta-Learning (MAML) algorithm to explicitly target air quality factors. The dataset employed includes features such as carbon monoxide concentration, sensor responses, and meteorological variables. Through extensive experimentation, our MAML-enhanced LSTM model demonstrates improved adaptability to new air quality forecasting tasks, particularly when data is limited. We present comprehensive results, including comparisons with traditional LSTM models, highlighting the efficacy of the proposed approach. This research contributes to the advancement of meta-learning techniques in the domain of environmental monitoring and offers insights into the potential of MAML for enhancing time series forecasting models.</p></div>","PeriodicalId":808,"journal":{"name":"Water, Air, & Soil Pollution","volume":"235 12","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water, Air, & Soil Pollution","FirstCategoryId":"6","ListUrlMain":"https://link.springer.com/article/10.1007/s11270-024-07549-9","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting air quality is essential for environmental monitoring and public health. In this work, we suggest a novel method for time series forecasting that uses Long Short-Term Memory (LSTM) networks and the Model-Agnostic Meta-Learning (MAML) algorithm to explicitly target air quality factors. The dataset employed includes features such as carbon monoxide concentration, sensor responses, and meteorological variables. Through extensive experimentation, our MAML-enhanced LSTM model demonstrates improved adaptability to new air quality forecasting tasks, particularly when data is limited. We present comprehensive results, including comparisons with traditional LSTM models, highlighting the efficacy of the proposed approach. This research contributes to the advancement of meta-learning techniques in the domain of environmental monitoring and offers insights into the potential of MAML for enhancing time series forecasting models.
期刊介绍：
Water, Air, & Soil Pollution is an international, interdisciplinary journal on all aspects of pollution and solutions to pollution in the biosphere. This includes chemical, physical and biological processes affecting flora, fauna, water, air and soil in relation to environmental pollution. Because of its scope, the subject areas are diverse and include all aspects of pollution sources, transport, deposition, accumulation, acid precipitation, atmospheric pollution, metals, aquatic pollution including marine pollution and ground water, waste water, pesticides, soil pollution, sewage, sediment pollution, forestry pollution, effects of pollutants on humans, vegetation, fish, aquatic species, micro-organisms, and animals, environmental and molecular toxicology applied to pollution research, biosensors, global and climate change, ecological implications of pollution and pollution models. Water, Air, & Soil Pollution also publishes manuscripts on novel methods used in the study of environmental pollutants, environmental toxicology, environmental biology, novel environmental engineering related to pollution, biodiversity as influenced by pollution, novel environmental biotechnology as applied to pollution (e.g. bioremediation), environmental modelling and biorestoration of polluted environments.
Articles should not be submitted that are of local interest only and do not advance international knowledge in environmental pollution and solutions to pollution. Articles that simply replicate known knowledge or techniques while researching a local pollution problem will normally be rejected without review. Submitted articles must have up-to-date references, employ the correct experimental replication and statistical analysis, where needed and contain a significant contribution to new knowledge. The publishing and editorial team sincerely appreciate your cooperation.
Water, Air, & Soil Pollution publishes research papers; review articles; mini-reviews; and book reviews.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
