Abstract
Machine learning techniques are employed to describe the temporal behavior of soil moisture using meteorological data as inputs. Three different Artificial Neural Network models, a feedforward Multi-Layer Perceptron, a Long-Short Term Memory and the Adaptive Network-based Fuzzy Inference System, are trained and their results are compared. The soil moisture is expressed in terms of Soil Water Index, derived from satellite retrievals, with the last known value also being used as input. The results are promising as the proposed methodology relies on free-access data with a worldwide coverage, allowing to easily estimate the forthcoming soil moisture. The knowledge of the expected value of this variable could be extremely useful for irrigation scheduling and it is the basis of Decision Support Systems to efficiently manage water resources in agriculture.
Highlights
Knowledge of Soil Moisture (SM) is fundamental in several scientific fields, such as rainfall-runoff modelling, landslide forecasting, soil nutrient cycling processes, drought monitoring, and agriculture [1]
At the end of the training phase, the best Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Adaptive Network-Based Fuzzy Inference System (ANFIS) models have been selected according to the abovementioned criteria, and a deeper analysis of the results has been carried out
The predictions seem to be less accurate in the correspondence of the remaining external regions of the observed ΔSWI, where the points deviate more markedly from the perfect forecast line: in these regions, the models tend to underestimate the amplitude of the real ΔSWI
Summary
Knowledge of Soil Moisture (SM) is fundamental in several scientific fields, such as rainfall-runoff modelling, landslide forecasting, soil nutrient cycling processes, drought monitoring, and agriculture [1]. In mid-latitude zones, the effects are visible as both long drought periods (and consequent yield reduction) or extremely intense and localised rainfall events, which could ruin a harvest and lead to crisis when flooding risk occurs In such a context, it is imperative to properly schedule irrigation practices, which rely on SM as a key variable determining the actual need for water of the crops. The schedule of gate-opening and irrigation planning is inconsistent with the actual crop need or with the forthcoming weather conditions. In this context, a Decision Support System (DSS) could be helpful. According to Power’s classification [8], it is proper to consider
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