Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management.

Journal: Journal of environmental management
PMID: 39889430

Abstract

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water management in crop fields. This study aims to enhance the forecasting of evapotranspiration (ET), potential evapotranspiration (PET), and crop water stress index (CWSI) using state-of-the-art deep learning techniques. This research integrates high-resolution climatic data from the ACCESS-ESM model and incorporates four shared socioeconomic pathways (SSPs) to represent a wide range of future climate scenarios. We employ feed forward neural networks (FFNNs), convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) to predict ET, PET, and CWSI. These findings reveal significant improvements in prediction accuracy, offering valuable insights for agricultural water management in Bangladesh. This approach provides a robust framework for optimizing irrigation practices and enhancing crop resilience against climate-induced water stress.

Authors

  • Mahfuzur Rahman
    Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh. Electronic address: mfz.rahman@iubat.edu.
  • Md Mehedi Hasan
    Nutrition and Clinical Services Division, International Center for Diarrheal Disease and Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
  • Md Anuwer Hossain
    International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
  • Utpal Kanti Das
    International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
  • Md Monirul Islam
    Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh. Electronic address: mmislam@iubat.edu.
  • Mohammad Rezaul Karim
    Department of Governmental System, Bangladesh Public Administration Training Centre (BPATC), Savar, Dhaka, Bangladesh.
  • Hamid Faiz
    Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China.
  • Zulfiqar Hammad
    Renewable Energy Research Institute, Kunsan National University, 558 Daehak-ro, Gunsan-si, Jeollabukdo, 54150, Republic of Korea.
  • Shamsher Sadiq
    Renewable Energy Research Institute, Kunsan National University, 558 Daehak-ro, Gunsan-si, Jeollabukdo, 54150, Republic of Korea.
  • Mehtab Alam
    Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China.

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