EGU23-8388, updated on 25 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

An ML-based Probabilistic Approach for Irrigation Scheduling

Shivendra Srivastava1, Nishant Kumar1, Arindam Malakar2, Sruti Das Choudhury3, Chittaranjan Ray4, and Tirthankar Roy1
Shivendra Srivastava et al.
  • 1Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, USA
  • 2School of Natural Resource and Nebraska Water Center, part of the Daugherty Water for Food Global Institute, University of Nebraska-Lincoln, USA
  • 3School of Natural Resources, University of Nebraska-Lincoln, USA
  • 4Nebraska Water Center, part of the Daugherty Water for Food Global Institute, University of Nebraska-Lincoln, USA

Globally, agriculture irrigation accounts for 70% of water use and is facing extensive and increasing water constraints. Well-designed irrigation scheduling can help determine the appropriate timing and water requirement for crop development and consequently improve water use efficiency. This research aims to assess the probability of irrigation needed for agricultural operations, considering soil moisture, evaporation, and leaf area index as indicators of crop water requirement. The decision on irrigation scheduling is taken based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation of each indicator are calculated by comparing the predicted values against the actual values, which are then used to calculate the error weights (normalized) of the three indicators for each month (to capture the seasonal variations). Third, the empirical distribution of each indicator is obtained for each month using the estimated error values, which are then adjusted based on the error weights calculated in the previous step. The probabilities of three threshold values (for each indicator) are considered, which correspond to three levels of irrigation requirement, i.e., low, medium, and high. The proposed approach provides a probabilistic framework for irrigation scheduling, which can significantly benefit farmers and policymakers in more informed decision-making related to irrigation scheduling.

How to cite: Srivastava, S., Kumar, N., Malakar, A., Choudhury, S. D., Ray, C., and Roy, T.: An ML-based Probabilistic Approach for Irrigation Scheduling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8388,, 2023.