{"id":8919,"date":"2026-07-03T17:42:34","date_gmt":"2026-07-03T09:42:34","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8919"},"modified":"2026-07-03T18:23:42","modified_gmt":"2026-07-03T10:23:42","slug":"jase-202610-33-029","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-029","title":{"rendered":"Assessment Method of Farmland Heavy Metal Content Based on Improved Elman and GWO Algorithm"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Hui Zhang1<\/sup><\/i><\/a>, Lu Qiao1<\/sup>, Weifeng Han2<\/sup>, Benyin Li2,<\/sup> and Yanfen Li3<\/sup><\/p>\n\n\n\n

1<\/sup>Henan Academy of Agricultural Sciences, Institute of Agricultural Economics and Rural Development, Zhengzhou 450002, China<\/p>\n\n\n\n

2<\/sup>Henan Academy of Agricultural Sciences, Institute of Plant Nutrition and Resource Environment, Zhengzhou, Henan, China<\/p>\n\n\n\n

3<\/sup>Academy of Agricultural and Forestry Sciences (Jiaozuo), Jiaozuo, Henan, China<\/p>\n<\/div>\n\n\n\n

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Received: March 4, 2026
Accepted:\u00a0May 25, 2026
Publication Date:\u00a0July 3, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Improved\u00a0RF\u00a0operation\u00a0flow chart\u00a0<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.029<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This study proposes a composite evaluation model based on improved Elman and mixed optimization strategy to address the core issues of insufficient dynamic sequence modeling ability, incomplete capture of multi factor nonlinear interaction relationships, and model parameters easily falling into local optima in existing methods for assessing heavy metal content in farmland soils. High precision dynamic evaluation of soil heavy metal content has been achieved by integrating gated loop units to improve random forest, convolutional block attention mechanism, and grey wolf optimizer and genetic algorithm to improve particle swarm optimization algorithm. The results showed that the evaluation accuracy of the CRF PGElman model reached 99.7%, with an error rate of 2.5%, an average absolute error of 0.21mg\/kg, a root mean square error of 0.33mg\/kg, and an R2<\/sup> of 0.97. In cross regional validation, the model achieved recognition accuracies of 0.99,0.98, and 0.99 for iron, chromium, and mercury, respectively. The research contribution lies in the construction of an intelligent hybrid assessment framework, which provides a new technological path for monitoring heavy metal pollution in farmland soil and quantifiable accuracy improvement for assessing heavy metal content in farmland soil under different geographical regions and pollution backgrounds. It has deployment value in practical monitoring applications.<\/p>\n\n\n\n

Keywords:\u00a0Assessment; Elman; Farmland heavy metal content; GRU; GWO; PSO<\/em><\/p>\n\n\n\n

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