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Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

机译：用引力搜索算法提高使用LSSVR河流预测的准确性

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摘要

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction.The effect of log transformation on prediction accuracy of the regressionmethods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.
机译：河流预测对于水资源规划和管理的许多应用是必不可少的。在本文中，与预测中的混合最小二乘支持向量回归 - 重力搜索算法（HLGSA）进行比较，将多变量自适应回归样条（MARS），模型5回归树（M5RT）和传统的多线性回归（CMLR）进行比较每月河流流动。在研究的第一部分中，所有三种回归方法都与彼此进行比较，以预测每个盆的河流。发现HLGSA方法比MARS，M5RT和CMLR更好地进行河流预测。在研究的第二部分也研究了对数转化对回归的预测准确性的影响。河流流量数据的日志转换显着提高了所有回归方法的预测精度。还发现Log HLGSA（LHLSGA）比其他回归方法更好。在该研究的第三部分中，使用附近的河流流量数据在河流估计中检查了LHLGSA和HLGSA方法的准确性。在所有申请的结果的基础上，发现LHLGSA和HLGSA可以成功地用于河流的预测和估计。

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