Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia

Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltm...
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Autor*in:	Iman Salehi Hikouei [verfasserIn] Jason Christian [verfasserIn] S. Sonny Kim [verfasserIn] Lori A. Sutter [verfasserIn] Stephan A. Durham [verfasserIn] Jidong J. Yang [verfasserIn] Charles Gray Vickery [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	saltmarsh halophyte bulk density random forest model redox potential machine learning

Übergeordnetes Werk:	In: Infrastructures - MDPI AG, 2017, 6(2021), 5, p 70
Übergeordnetes Werk:	volume:6 ; year:2021 ; number:5, p 70

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/infrastructures6050070

Katalog-ID:	DOAJ056057849

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520			\|a Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.
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language	English
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author	Iman Salehi Hikouei
spellingShingle	Iman Salehi Hikouei misc saltmarsh misc halophyte misc bulk density misc random forest model misc redox potential misc machine learning misc Technology misc T Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia
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topic_title	Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia saltmarsh halophyte bulk density random forest model redox potential machine learning
topic	misc saltmarsh misc halophyte misc bulk density misc random forest model misc redox potential misc machine learning misc Technology misc T
topic_unstemmed	misc saltmarsh misc halophyte misc bulk density misc random forest model misc redox potential misc machine learning misc Technology misc T
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title	Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia
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title_full	Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia
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author_browse	Iman Salehi Hikouei Jason Christian S. Sonny Kim Lori A. Sutter Stephan A. Durham Jidong J. Yang Charles Gray Vickery
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title_sort	use of random forest model to identify the relationships among vegetative species, salt marsh soil properties, and interstitial water along the atlantic coast of georgia
title_auth	Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia
abstract	Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.
abstractGer	Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.
abstract_unstemmed	Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.
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title_short	Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia
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up_date	2024-07-03T18:37:57.473Z
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Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia

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