Ecological Informatics最新文献

{"title":"Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions","authors":"Guangpo Geng ,&nbsp;Qian Gu ,&nbsp;Hongkui Zhou ,&nbsp;Bao Zhang ,&nbsp;Zuxin He ,&nbsp;Ruolin Zheng","doi":"10.1016/j.ecoinf.2024.102972","DOIUrl":"10.1016/j.ecoinf.2024.102972","url":null,"abstract":"<div><div>Timely and reliable crop yield estimation is vital for ensuring both global and regional food security. Previous studies have primarily used process-based crop models or statistical regression-based models for crop yield estimates. However, these model types possess limitations, particularly in accounting for specific extreme climate events that occur during the growth stage. In this study, remote sensing data, climate data, and soil moisture data from the winter wheat growth period in northern China from 2003 to 2017 were used to construct a crop yield simulation model based on the Random Forest (RF) algorithm. The effect of drought on winter wheat yield was quantitatively evaluated by calculating the fitting accuracy of the RF model, analyzing the importance of the factors influencing yield simulations, identifying a typical drought event, and determining the yield estimation accuracy as well as the percent yield loss (PYL) under drought conditions. The results indicated that solar-induced chlorophyll fluorescence (SIF) could characterize drought stress on winter wheat yield. The fitting accuracy of the RF yield simulation model was relatively high (R² = 0.72). Among all climate factors, SIF, enhanced vegetation index, and soil moisture were significant factors affecting wheat yield, exerting greater effect than those of all other climate factors. Furthermore, 2011 was identified as a typical drought year in the winter wheat area of northern China. The RF model simulated the accuracy of winter wheat yield for 2011 with an R² of 0.80. The RF model simulation revealed that the yield simulation accuracy of winter wheat under drought conditions was 90.64 %. The mean simulated PYL due to drought was 5.6 %, aligning closely with the mean actual PYL of 6.1 %. This suggested that the RF model was feasible for simulating crop yields and tracking yield variations by incorporating environmental variables, especially SIF data, under drought conditions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102972"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling height to crown base using non-parametric methods for mixed forests in China","authors":"Zeyu Zhou ,&nbsp;Huiru Zhang ,&nbsp;Ram P. Sharma ,&nbsp;Xiaohong Zhang ,&nbsp;Linyan Feng ,&nbsp;Manyi Du ,&nbsp;Lianjin Zhang ,&nbsp;Huanying Feng ,&nbsp;Xuefan Hu ,&nbsp;Yang Yu","doi":"10.1016/j.ecoinf.2024.102957","DOIUrl":"10.1016/j.ecoinf.2024.102957","url":null,"abstract":"<div><div>The height to crown base (HCB) of a tree is a vital characteristic that reflects the self-thinning ability of a tree, and it is used to determine the crown size. and predict the crown recession rate. This study simulated the HCB of Spruce fir broadleaved mixed forest in Northeast China using four non-parametric model approaches: generalized additive model, Cubist, boosted regression tree (BRT), and multiple adaptive regression spline. Because of the different genetic characteristics and growth patterns of different tree species, species-specific tree groups were formed, and the HCB of each species-specific group was simulated by the different models. Relative importance and partial dependence analyses were performed to identify the primary HCB predictors (including tree, stand, stand spatial structure, density and competition factors) and their relationships with the HCB of the four tree species groups. The relative importance was higher for individual tree variables (77.54 %, 31.02 %, 31.12 %, and 73.69 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) and stand variables (5.00 %, 20.34 %, 11.03 %, and 8.71 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) compared with stand spatial structure variables (4.57 %, 12.14 %, 21.91 %, and 5.89 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), density indexes variables (2.17 %, 1.28 %, 4.05 %, and 2.87 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), and tree species variables (10.79 %, 35.20 %, 31.90 %, and 8.84 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively). BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102957"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate estimation of Jujube leaf chlorophyll content using optimized spectral indices and machine learning methods integrating geospatial information","authors":"Nigela Tuerxun ,&nbsp;Sulei Naibi ,&nbsp;Jianghua Zheng ,&nbsp;Renjun Wang ,&nbsp;Lei Wang ,&nbsp;Binbin Lu ,&nbsp;Danlin Yu","doi":"10.1016/j.ecoinf.2024.102980","DOIUrl":"10.1016/j.ecoinf.2024.102980","url":null,"abstract":"<div><div>Leaf chlorophyll content (LCC) is vital for photosynthesis and ecosystem functioning; it influences carbon, water, and energy exchanges while serving as an indicator of photosynthetic activity and nitrogen levels in precision agriculture. Hyperspectral data enable precise LCC monitoring by extracting spectral indices through optimal band combination (OBC) and predicting LCC with machine learning. However, OBC faces dimensionality issues, and machine learning models often overlook geographical influences, potentially reducing prediction accuracy. This study hypothesizes that developing spectral indices from important wavelengths and integrating geospatial data into machine learning models can address these issues and increase prediction accuracy. To test this hypothesis, a framework was developed that first uses elastic net (EN) and the successive projection algorithm (SPA) for wavelength selection, followed by spectral index creation with OBC and ranking with random forest (RF). Support vector regression (SVR), random forest regression (RFR), and geographically weighted least squares support vector regression (GWLS-SVR) were then used to assess the prediction accuracy. Finally, the optimal variables and regression model were identified. The results revealed that the EN- and SPA-based indices had stronger correlations and importance than defined indices. The double-difference index (DDn) and the anti-reflectance index (ARI) are the most robust three-dimensional and two-dimensional spectral indices, respectively. GWLS-SVR requires fewer indices (1–4) to achieve optimal results, with EN-DDn (2<em>R</em>₅₁₉-<em>R</em>₇₇₅-<em>R</em>₉₃₆)-GWLS-SVR performing best (R² = 0.95, RMSE = 0.61, PBIAS = -0.02). This research presents a robust framework with strong adaptability for estimating LCC in a specific study area and region, demonstrating substantial potential for the precise estimation of agroforestry vegetation parameters.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102980"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A remote sensing-based strategy for mapping anthropogenic urban surface ecological poorness zones (AUSEPZ): A case study of Lisbon City","authors":"Mohammad Karimi Firozjaei ,&nbsp;Naeim Mijani ,&nbsp;Peter M. Atkinson","doi":"10.1016/j.ecoinf.2024.102975","DOIUrl":"10.1016/j.ecoinf.2024.102975","url":null,"abstract":"<div><div>Anthropogenic activities play a crucial role in the formation and intensification of Urban Surface Ecological Poorness Zones (USEPZ). This study introduces a methodology for assessing the spatiotemporal fluctuations of Anthropogenic USEPZ (AUSEPZ), using Lisbon city and the Setúbal district as a case study to demonstrate its effectiveness. By integrating data from various surface characteristics through the Comprehensive Ecological Evaluation Index (CEEI), Surface Ecological Condition (SEC) maps were developed, and their spatial and temporal variations were analyzed. Additionally, a feature space was established between the Impervious Surface Percentage (ISP) and CEEI to calculate AUSEPZ intensity across different years. The findings revealed that the mean CEEI of Lisbon increased by 0.41 between 1986 and 2023. During this period, the proportions of SEC classified as Excellent, Very Good, Good, Fair, and Poor changed by −52 %, −13 %, +107 %, +444 %, and + 1134 %, respectively. The AUSEPZ intensity values for Lisbon were 0.32, 0.39, 0.46, 0.52, 0.57, and 0.63 for the years 1986, 1994, 2001, 2008, 2015, and 2023, respectively. The intensification of human activities, driven by urban expansion and population growth, has significantly contributed to the deterioration of SEC in Lisbon over recent years. These findings provide valuable insights for urban planners, policymakers, and stakeholders, enabling the design of targeted strategies to mitigate the impacts of urbanization and enhance ecological conditions in urban areas.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102975"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatiotemporal optimization engine for prescribed burning in the Southeast US","authors":"Reetam Majumder ,&nbsp;Adam J. Terando ,&nbsp;J. Kevin Hiers ,&nbsp;Jaime A. Collazo ,&nbsp;Brian J. Reich","doi":"10.1016/j.ecoinf.2024.102956","DOIUrl":"10.1016/j.ecoinf.2024.102956","url":null,"abstract":"<div><div>Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102956"},"PeriodicalIF":5.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"net.raster: Interaction network metrics for raster data","authors":"Cynthia Valéria Oliveira ,&nbsp;Gabriela Alves-Ferreira ,&nbsp;Flávio Mariano Machado Mota ,&nbsp;Daniela Custódio Talora ,&nbsp;Neander Marcel Heming","doi":"10.1016/j.ecoinf.2024.102969","DOIUrl":"10.1016/j.ecoinf.2024.102969","url":null,"abstract":"<div><div>The interaction among species from different trophic levels is essential for ecosystem functioning and the use of bipartite networks is often useful for improving our understanding of multiple ecological processes, such as seed dispersal, pollination, and predation. Still, we are just paving ways to better understand spatial variation and macroecological aspects of interaction diversity. Here we introduce <em>net.raster</em>, an R package to calculate network and species-level metrics using rasterized presence-absence data and bipartite interaction networks as input, aiming to place species interaction studies into a spatial perspective. First, we focus on the spatialization of the functions and arguments from the <em>bipartite</em> R package using the <em>terra</em> package. Then, we enhance the visualisation of interaction patterns across space by allowing a raster layer of species interactions in addition to species distribution models (SDM). To date, all available packages that compute mutualistic network metrics rely only on matrices, or edge lists and network graphs derived from them. The <em>net.raster</em> package applies the calculations for each cell of a raster, allowing users to extrapolate known interactions across space and to visualise spatial patterns of bipartite network descriptors. The resulting rasters of interaction metrics are based mainly on the geographical extrapolation of interaction records between pairs of species and the resulting calculations use co-occurrence as a proxy for an interaction between species. Like other network analysis packages, <em>net.raster</em> allows users to calculate network topography indices using: a) the entire web, b) selecting the lower or upper level of each group, or c) selecting each species, choosing both levels or one level of interest at a time. Thus, the spatial processing and visualisation of fundamental bipartite networks provided by <em>net.raster</em> may fill a current gap in macroecological and biogeographical research and enable the understanding of the spatial variation of interaction networks. It also may open other questions in the macroecological and biogeographical study of networks, inspiring new insights into the conservation of important ecosystem services, such as seed dispersal and pollination.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 102969"},"PeriodicalIF":5.8,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"dataFishing: An efficient Python tool and user-friendly web-form for mining mitochondrial and chloroplast sequences, taxonomic, and biodiversity data","authors":"Luan Rabelo ,&nbsp;Davidson Sodré ,&nbsp;Oscar David Albito Balcázar ,&nbsp;Murilo Furtado do Rosário ,&nbsp;Aurycéia Jaquelyne Guimarães-Costa ,&nbsp;Grazielle Gomes ,&nbsp;Iracilda Sampaio ,&nbsp;Marcelo Vallinoto","doi":"10.1016/j.ecoinf.2024.102970","DOIUrl":"10.1016/j.ecoinf.2024.102970","url":null,"abstract":"<div><div>NCBI GenBank and BOLD Systems are important databases for biodiversity research, in which the deposited data can be used for various purposes, such as species identification analysis, evolutionary studies, biodiversity monitoring, as well as assessing the effects of possible climate changes on species distributions. Other information, such as taxonomy, collection site locations, and conservation status, is often critical for these studies. Some databases, such as GBIF, BOLD Systems, and GenBank, provide data on the taxonomy, habitat, and geographic distribution of various taxonomic groups, while others, such as WoRMS and IUCN, have specific data on marine species and conservation status. However, depending on the taxonomic group studied, searches in these databases can encompass dozens or hundreds of queries, forcing researchers to conduct extensive searches in each database, which is a time-consuming and error-prone process. To facilitate and automate access to this information, we introduce dataFishing, a Python script and a web form. dataFishing is faster and more efficient than other R packages, such as <em>bold</em>, <em>taxize</em>, <em>rgbif</em>, <em>rredlist</em>, and <em>worrms</em>, for obtaining taxonomic information from the consulted databases. Moreover, it allows the retrieval of DNA sequences, common names, synonyms, conservation status, and species occurrence points. This tool is free and will enable a more systematized and time-efficient search, which tends to facilitate such data inquiries.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102970"},"PeriodicalIF":5.8,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping an invasive grass in the northwestern US with fused satellite time series and biophysical features","authors":"Ty C. Nietupski ,&nbsp;Hailemariam Temesgen ,&nbsp;Becky K. Kerns","doi":"10.1016/j.ecoinf.2024.102973","DOIUrl":"10.1016/j.ecoinf.2024.102973","url":null,"abstract":"<div><div>The introduction and spread of the invasive annual grass <em>Ventenata dubia</em> (ventenata) has incited concern from land managers in the Inland Northwestern United States. Maps describing ventenata's distribution would be a valuable management asset but have not been developed. Techniques using satellite time-series have been used to detect annual grasses' unique phenological qualities (timing of biological events); however, detection is complicated by the co-occurrence of phenologically similar species. This study aimed to examine the capability of land surface phenology derived from fused satellite imagery to map and gain insight into the ventenata invasion. We evaluated the influence of land surface phenology, climate, and topo-edaphic predictors on ventenata classification and examined differences in the distributions predicted from three random forest models: 1) hybrid (phenology, climate, topo-edaphic), 2) bioclimatic (climate, topo-edaphic), and 3) phenology (phenology). The hybrid model indicated that 7.7 % (5454 km²) of the Blue Mountains Ecoregion may contain heavy ventenata invasion and that many populations were located in forest/non-forest transition zones and forest openings. The phenology model predicted ventenata populations in regions occupied by other annual grasses, suggesting that land surface phenology characteristics alone could not differentiate ventenata from other invasive annual grasses. The bioclimatic model identified suitable habitat but overpredicted invasion extent in heavily treed areas. These results suggested that incorporating phenology with climatic predictors effectively differentiates invasive annual grasses when phenological patterns are similar, but habitat requirements differ.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 102973"},"PeriodicalIF":5.8,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining habitat selection, behavioural states, and individual variation to predict fish spatial usage near a barrier","authors":"Rachel Mawer ,&nbsp;Jelger Elings ,&nbsp;Stijn P. Bruneel ,&nbsp;Ine S. Pauwels ,&nbsp;Eliezer Pickholtz ,&nbsp;Renanel Pickholtz ,&nbsp;Johan Coeck ,&nbsp;Peter L.M. Goethals","doi":"10.1016/j.ecoinf.2024.102967","DOIUrl":"10.1016/j.ecoinf.2024.102967","url":null,"abstract":"<div><div>Riverine barriers are threatening freshwater fish migration, with major impacts on fish populations. Effective management requires understanding of fish movement and behaviour as they approach a barrier and fish pass, which can inform optimal mitigation options and barrier management. Here, the movements of upstream migrating barbel <em>Barbus barbus</em> and grayling <em>Thymallus thymallus</em> near a barrier were analysed and results used to develop predictive models. Fish were tracked via 2D acoustic telemetry. Hidden Markov models were used to distinguish behavioural states and step selection functions were applied to determine habitat selection by the fish in each state. Model results were explored to assess the benefits of including behavioural state and understand state-specific habitat preferences, then cross-validated and used to develop an individual based model to predict fish spatial usage. Little difference existed in habitat selection between states and individual variation was high, limiting general trends that could be described. Overall, barbel preferred deeper or faster water while for grayling, few trends could be described. Under the tested flow conditions, high spatial usage was predicted in the area directly downstream of the barrier. In addition, barbel usage was high in the area by and downstream of the fish pass entrance but not for grayling, which may indicate a need to improve pass attractiveness for grayling. The predictive model produced directed upstream movements of fish similar to those expected for upstream migrating freshwater fish, highlighting model potential for fish passage applications in future iterations. The high individual variability in fish behaviour drives the need for individual-based approaches for predicting fish movement.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102967"},"PeriodicalIF":5.8,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and implementation of EcoDecibel: A low-cost and IoT-based device for noise measurement","authors":"Ling-Jyh Chen ,&nbsp;Sakshi Saraswat ,&nbsp;Fu-Shiang Ching ,&nbsp;Chih-Yi Su ,&nbsp;Hsin-Lan Huang ,&nbsp;Wen-Chi Pan","doi":"10.1016/j.ecoinf.2024.102968","DOIUrl":"10.1016/j.ecoinf.2024.102968","url":null,"abstract":"<div><h3>Background</h3><div>Noise pollution is a growing concern for public health and the environment. Traditional noise monitoring methods often have limitations due to short-term measurements and high costs.</div></div><div><h3>Objective</h3><div>This study aims to develop and validate EcoDecibel, a low-cost, IoT-based sensor system for continuous noise monitoring, addressing the gaps in existing noise measurement technologies.</div></div><div><h3>Methods</h3><div>EcoDecibel was compared with Class 1 and Class 2 sound level meters in various conditions. The system was deployed across three environmental sites in <em>Sanzhi</em> District, Taiwan, for one week. Time-series prediction and forecasting models (SARIMA, Prophet, LSTM) were applied to the noise data to predict and forecast noise levels.</div></div><div><h3>Results</h3><div>EcoDecibel demonstrated strong correlation, yielding R² values of 0.948 and 0.983 in comparison with Class 1 and Class 2 sound level meters and was able to monitor and forecast daily noise patterns effectively. The system performed well across different environments and was capable of continuous real-time monitoring.</div></div><div><h3>Conclusions</h3><div>EcoDecibel presents a cost-effective and reliable solution for long-term environmental noise monitoring. The system is suitable for use in epidemiological studies investigating the relationship between noise exposure and public health outcomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"85 ","pages":"Article 102968"},"PeriodicalIF":5.8,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}