{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:49:14Z","timestamp":1775738954733,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran. In total, 1595 composite surficial soil samples were collected, and 64 environmental covariates derived from terrain, climatic, remotely sensed, and categorical datasets were used as predictors. Models were tested using a repeated 10-fold nested cross-validation approach. The results indicate that the hybridized ANN methods were far superior to the reference approach using ANN with a backpropagation training algorithm (BP-ANN). Furthermore, the MBO-ANN approach was consistently determined to be the best approach and yielded the lowest error and uncertainty. The MBO-ANN model improved the predictions in terms of RMSE by 20% for clay, 10% for silt, and 24% for sand when compared to BP-ANN. The physiographical units, soil types, geology maps, rainfall, and temperature were the most important predictors of PSFs, followed by the terrain and remotely sensed data. This study demonstrates the effectiveness of bio-inspired algorithms for improving ANN models. The outputs of this study will support and inform sustainable soil management practices, agro-ecological modeling, and hydrological modeling for the Mazandaran Province of Iran.<\/jats:p>","DOI":"10.3390\/rs13051025","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T12:12:18Z","timestamp":1615205538000},"page":"1025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6624","authenticated-orcid":false,"given":"Ruhollah","family":"Taghizadeh-Mehrjardi","sequence":"first","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan 8951656767, Iran"},{"name":"CRC 1070 Ressource Culture, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa","family":"Emadi","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Crop Sciences, Sari Agricultural Sciences and Natural Resources University, Sari 4818168984, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Cherati","sequence":"additional","affiliation":[{"name":"Soil and Water Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, AREEO, Sari 4849155356, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brandon","family":"Heung","sequence":"additional","affiliation":[{"name":"Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Halifax, NS B3H 4R2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Department of Ecology, Technische Universit\u00e4t Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany"},{"name":"School of Economics and Business, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2602","authenticated-orcid":false,"given":"Thomas","family":"Scholten","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 Ressource Culture, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.4236\/ojss.2019.99010","article-title":"Influence of Clay Minerals on Some Soil Fertility Attributes: A Review","volume":"9","author":"Kome","year":"2019","journal-title":"Open J. 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