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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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  • Chenyi Jiang

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
    School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
    These authors contributed equally to this work.)

  • Liangliang Zhang

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
    School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
    These authors contributed equally to this work.)

  • Dong Liu

    (School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
    Joint International Research Laboratory of Habitat Health of Black Soil in Cold Regions, Ministry of Education, Northeast Agricultural University, Harbin 150030, China
    Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin 150030, China)

  • Mo Li

    (School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Xiaochen Qi

    (School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Tianxiao Li

    (School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Song Cui

    (School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
    Research Center for Eco-Environment Protection of Songhua River Basin, Northeast Agricultural University, Harbin 150030, China)

Abstract

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 km 2 . 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.

Suggested Citation

  • Chenyi Jiang & Liangliang Zhang & Dong Liu & Mo Li & Xiaochen Qi & Tianxiao Li & Song Cui, 2025. "Evolution Characteristics and Influencing Factors of Agricultural Drought Resilience: A New Method Based on Convolutional Neural Networks Combined with Ridge Regression," Sustainability, MDPI, vol. 17(11), pages 1-29, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4808-:d:1662909
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    References listed on IDEAS

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