Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 5; p. 1025
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Emadi, Mostafa, Cherati, Ali, Heung, Brandon, Mosavi, Amir, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.03.2021
• Subjects: agroecology; algorithms; artificial intelligence; Chiroptera; clay; Danaus plexippus; data collection; evolutionary algorithms; hybrid machine learning; hybridization; Iran; landscapes; neural networks; particle size; particle size fractions; rain; remote sensing; sand; silt; soil management; soil texture; spatial distribution; sub-humid regions; temperature; uncertainty; Iran

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural n...