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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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 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.
Funding Information
  • Open Research Fund Program of the State Key Laboratory of Hydroscience and Engineering (sklhse-2023-A-04, mklhs-2023-03, 52309012, 52179008, 51579044, 41071053, 2023YFD1501004, 2024YFD1501700, LH2023E003)
  • Open Research Fund Program of Key Laboratory of the Hydrosphere of the Ministry of Water Resources (sklhse-2023-A-04, mklhs-2023-03, 52309012, 52179008, 51579044, 41071053, 2023YFD1501004, 2024YFD1501700, LH2023E003)
  • National Natural Science Foundation of China (sklhse-2023-A-04, mklhs-2023-03, 52309012, 52179008, 51579044, 41071053, 2023YFD1501004, 2024YFD1501700, LH2023E003)
  • National Key R&D Program of China (sklhse-2023-A-04, mklhs-2023-03, 52309012, 52179008, 51579044, 41071053, 2023YFD1501004, 2024YFD1501700, LH2023E003)
  • Joint guidance project of Heilongjiang Natural Science Foundation (sklhse-2023-A-04, mklhs-2023-03, 52309012, 52179008, 51579044, 41071053, 2023YFD1501004, 2024YFD1501700, LH2023E003)

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