论文标题
使用人工神经网络和水电学变量预测短期和长期干旱
Prediction of short and long-term droughts using artificial neural networks and hydro-meteorological variables
论文作者
论文摘要
干旱是一种自然的威胁，在人类生活的各个方面具有许多破坏性影响。准确的干旱预测是帮助决策者制定干旱风险管理策略的有希望的步骤。为了实现此目的，选择适当的模型在预测方法中起着重要作用。在这项研究中，通过在不同时间尺度上使用标准化降水指数（SPI），包括在伊朗的Babriz City的3、6、12、24和48个月，使用标准化降水指数（SPI）来预测干旱的短期和长期模型。为此，各种水电学变量的计算出的SPI和时间序列的不同组合，例如1992年至2010年的降水，风速，相对湿度和阳光小时，用于训练ANN模型。为了比较模型性能，在本研究中使用了一些众所周知的措施，即RMSE，平均绝对误差（MAE）和相关系数（CC）。结果表明，所有水电学变量的应用显着改善了在不同时间尺度下的SPI预测。
Drought is a natural creeping threat with numerous damaging effects in various aspects of human life. Accurate drought prediction is a promising step in helping policy makers to set drought risk management strategies. To fulfill this purpose, choosing appropriate models plays an important role in predicting approach. In this study, different models of Artificial Neural Network (ANN) are employed to predict short and long-term of droughts by using Standardized Precipitation Index (SPI) at different time scales, including 3, 6, 12, 24 and 48 months in Tabriz city, Iran. To this end, different combination of calculated SPI and time series of various hydro-meteorological variables, such as precipitation, wind velocity, relative humidity and sunshine hours for years 1992 to 2010 are used to train the ANN models. In order to compare the models performances, some well-known measures, namely RMSE, Mean Absolute Error (MAE) and Correlation Coefficient (CC) are utilized in the present study. The results illustrate that the application of all hydro-meteorological variables significantly improves the prediction of SPI at different time scales.
