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Evolution Characteristics and Influencing Factors of Agricultural Drought Resilience: A New Method Based on Convolutional Neural Networks Combined with Ridge Regression

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Název: Evolution Characteristics and Influencing Factors of Agricultural Drought Resilience: A New Method Based on Convolutional Neural Networks Combined with Ridge Regression
Autoři: Chenyi Jiang, Liangliang Zhang, Dong Liu, Mo Li, Xiaochen Qi, Tianxiao Li, Song Cui
Zdroj: Sustainability ; Volume 17 ; Issue 11 ; Pages: 4808
Informace o vydavateli: Multidisciplinary Digital Publishing Institute
Rok vydání: 2025
Sbírka: MDPI Open Access Publishing
Témata: drought resilience evaluation, DPSIR conceptual model, Adam with weight decay, convolutional neural network, Kepler optimization algorithm, ridge regression
Popis: To enhance the precision of regional agricultural drought resilience evaluation, a convolutional neural network optimized with Adam with weight decay (AdamW–CNN) was constructed. Based on local agricultural economic development regulations and utilizing the Driving Force–Pressure–State–Impact–Response (DPSIR) conceptual model, sixteen indicators of agricultural drought resilience were selected. Subsequently, data preprocessing was conducted for Qiqihar City, Heilongjiang Province, China, which encompasses an area of 42,400 km2. The drought resilience was accurately assessed based on the developed AdamW–CNN model from 2000 to 2021 in the study area. The key driving factors behind the spatiotemporal evolution of drought resilience were identified using gray relational analysis, and the future evolution trend of agricultural drought resilience was revealed through Ridge regression analysis improved by the Kepler optimization algorithm (KOA–Ridge). The results indicated that the agricultural drought resilience in Qiqihar City exhibited a trend of initial fluctuations, followed by a significant increase in the middle phase, and then stable development in the later stage. Precipitation, investment in the primary industry, grain output per unit of cultivated area, per capita cultivated land area, and the proportion of effective irrigation area were the primary driving factors in the study area. By simulating the drought resilience index of four typical regions and analyzing its evolution, it was found that the AdamW–CNN model, combined with the KOA–Ridge model, has greater advantages over the RMSProp-CNN model and the CNN model in terms of fit, stability, reliability, and evaluation accuracy. These findings provide a robust model for measuring agricultural drought resilience, offering valuable insights for regional drought prevention and management.
Druh dokumentu: text
Popis souboru: application/pdf
Jazyk: English
Relation: https://dx.doi.org/10.3390/su17114808
DOI: 10.3390/su17114808
Dostupnost: https://doi.org/10.3390/su17114808
Rights: https://creativecommons.org/licenses/by/4.0/
Přístupové číslo: edsbas.B6EC068B
Databáze: BASE
Popis
Abstrakt:To enhance the precision of regional agricultural drought resilience evaluation, a convolutional neural network optimized with Adam with weight decay (AdamW–CNN) was constructed. Based on local agricultural economic development regulations and utilizing the Driving Force–Pressure–State–Impact–Response (DPSIR) conceptual model, sixteen indicators of agricultural drought resilience were selected. Subsequently, data preprocessing was conducted for Qiqihar City, Heilongjiang Province, China, which encompasses an area of 42,400 km2. The drought resilience was accurately assessed based on the developed AdamW–CNN model from 2000 to 2021 in the study area. The key driving factors behind the spatiotemporal evolution of drought resilience were identified using gray relational analysis, and the future evolution trend of agricultural drought resilience was revealed through Ridge regression analysis improved by the Kepler optimization algorithm (KOA–Ridge). The results indicated that the agricultural drought resilience in Qiqihar City exhibited a trend of initial fluctuations, followed by a significant increase in the middle phase, and then stable development in the later stage. Precipitation, investment in the primary industry, grain output per unit of cultivated area, per capita cultivated land area, and the proportion of effective irrigation area were the primary driving factors in the study area. By simulating the drought resilience index of four typical regions and analyzing its evolution, it was found that the AdamW–CNN model, combined with the KOA–Ridge model, has greater advantages over the RMSProp-CNN model and the CNN model in terms of fit, stability, reliability, and evaluation accuracy. These findings provide a robust model for measuring agricultural drought resilience, offering valuable insights for regional drought prevention and management.
DOI:10.3390/su17114808