Using Machine Learning Methods for Agricultural Water Cycle Assessment

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 3516

Special Issue Editor

Collage of Water Resource and Architectural Engineering, Northwest A&F University, Xianyang, China
Interests: machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the dual pressures of global climate change and population growth, the shortage of agricultural water resources is an issue of increasing severity. The traditional methods for evaluating agricultural water resources are no longer able to meet the needs of modern agricultural production. Machine learning can extract useful features and patterns from massive amounts of data to predict the supply and demand of agricultural water resources. On this basis, by analyzing crop growth data and environmental factors (such as temperature, rainfall, and soil moisture), the growth and water demand patterns of crops are predicted. This Special Issue aims to explore the application of machine learning in agricultural water resource assessment to improve the accuracy and efficiency of assessment, as well as provide theoretical support for the scientific management and efficient utilization of agricultural water resources.

Dr. Ze Liu
Guest Editor

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Keywords

  • agricultural water resources
  • machine learning
  • water demand
  • big data
  • features

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Published Papers (2 papers)

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Research

32 pages, 6150 KiB  
Article
Investigating the Performance of the Informer Model for Streamflow Forecasting
by Nikos Tepetidis, Demetris Koutsoyiannis, Theano Iliopoulou and Panayiotis Dimitriadis
Water 2024, 16(20), 2882; https://doi.org/10.3390/w16202882 - 10 Oct 2024
Cited by 1 | Viewed by 1419
Abstract
Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of [...] Read more.
Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. The predictive capabilities of the Informer model are explored and compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in streamflow time series. The analysis is extended to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. For a forecast horizon of 168 days, the Informer model achieves an NSE of 0.8 and maintains a MAPE below 10%, while the second-best model (LSTM) only achieves −0.63 and 25%, respectively. Furthermore, it is observed that the dependence structure of time series, as expressed by the climacogram, affects the performance of the Informer network. Full article
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21 pages, 14185 KiB  
Article
An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model
by Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim and Yangwon Lee
Water 2024, 16(18), 2661; https://doi.org/10.3390/w16182661 - 18 Sep 2024
Viewed by 1724
Abstract
Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data [...] Read more.
Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. Full article
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