Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Advanced satellite technology and algorithms are making substantial progress in meeting the need for improved environmental monitoring of coastal waterways. Integrating high-resolution satellites with in-situ radiometric equipment is essential for effectively monitoring algal blooms and managing coastal resources. Our work has built a model to examine geographical and temporal fluctuations in chlorophyll-a concentration in Bushehr Bay, Persian Gulf, Iran, using radiometric data and high-resolution remote sensing. In this study, we used twenty-four bio-optical features for analysis. After evaluating and selecting the most important features, we used the top five features to estimate chlorophyll-a concentration using machine learning algorithms. Likewise, the model could effectively investigate our climatology of chlorophyll in the study area. Our findings provide a dependable approach to monitor the environmental effect of chlorophyll-a and enhance water quality and regional management of primary production in coastal waters. This proposed proxy may be implemented in comparable places globally.
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Source |
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http://dx.doi.org/10.1016/j.marpolbul.2024.117263 | DOI Listing |
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