REFERENCE EVAPOTRANSPIRATION FORECASTING BY ARTIFICIAL NEURAL NETWORKS

ABSTRACT: Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable.摘要：蒸散发（Evapotranspiration, ET）是农业系统水量平衡的核心组成部分，也是水文循环中最为活跃的变量。当前针对巴西圣保罗州北部地区，采用人工神经网络（Artificial Neural Networks, ANNs）提前1天开展蒸散发预报的研究较为少见。为此，本研究旨在针对巴西圣保罗州甘蔗主产区雅布蒂卡瓦尔（Jaboticabal）地区，预测参考蒸散发量。我们收集了该地区2002—2012年的历史数据序列，涵盖平均气温、风速、净辐射、土壤热通量及日相对湿度。参考蒸散发量采用彭曼-蒙特斯（Penman-Monteith）法进行估算。为实现参考蒸散发量的预报，我们采用了传统人工神经网络中的前馈式多层感知机（Multi-Layer Perceptron, MLP）模型。我们对隐藏层与输出层的神经元数量开展了多种拓扑结构与参数组合的测试，直至得到精度最优的模型。我们将数据集按75%、25%的比例划分为训练集（2002—2010年）与测试集（2011—2013年）。评估人工神经网络模型性能的指标包括准确率、精确率与趋势一致性。研究结果表明，仅以气温作为输入变量，借助人工神经网络即可在全年任意时段提前1天精准估算参考蒸散发量。