Abstract
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions.
Highlights
Access to water is a fundamental right of today’s societies
Studies show that artificial neural network (ANN)-based models perform better than more traditional techniques because they capture the non-linear nature of the problem [27]
Mechanisms are in place to prevent evaporation losses in storage reservoirs to make the best use of this scarce resource
Summary
Access to water is a fundamental right of today’s societies It is a vital resource for living beings and for the economic performance, growth, and viability of many business sectors [1]. The application of new technologies in these sectors, such as agriculture, guides the revolution of a society with an active role to face the related water scarcity problems [3]. Precision agriculture promotes the deployments of new technologies such as IoT or Artificial Intelligence in the sector of agriculture This discipline covers issues ranging from pest detection to water saving, frost risk management, harvesting, and climate control of greenhouses, among others. Since the early 1990s, ML techniques have started to be used in a wide range of problems related to water resources management such as precipitation forecasting, rainfallrunoff modelling, groundwater modelling, water quality assessment, sediment load prediction, and evaporation modelling. Since the evaporation problem is complex, researchers continue to work on obtaining accurate and reliable predictive models, highlighting the importance of reducing the number of measures used to obtain simpler models
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