An approach for flood flow prediction utilizing new hybrids of ANFIS with several optimization techniques: a case study

IF 2.6 4区 环境科学与生态学 Q2 WATER RESOURCES
Negin Ahmadi, Sina Fard Moradinia
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引用次数: 0

Abstract

Using machine learning methods is efficient in predicting floods in areas where complete data is not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harris Hawks Optimization (HHO) and Arithmetic Optimization Algorithm (AOA), to predict the flood of Shahrchay River in the northwest of Iran. The data used included the daily data of precipitation, evaporation, and runoff in the years 2016 and 2017, where 70% of the data were used for model training and the rest for testing the models. The results showed that although the ANFIS model provided values with high errors in several steps, especially in steps with maximum or minimum values, the use of HHO and AOA optimization algorithms resulted in a significant reduction in the error values. The ANFIS-AOA model utilizing an input scenario including the flow in the previous one to three days exerted the most promising results in the test data, with Nash Sutcliffe Efficiency (NSE) Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.93, 1.34, and 0.69, respectively. According to Taylor's diagram, the ANFIS-AOA hybrid algorithm predicts flood values with greater performance than the other models.

利用 ANFIS 与若干优化技术的新混合体进行洪水流量预测的方法:案例研究
在无法获得完整数据的地区,使用机器学习方法可以有效预测洪水。因此,本研究考虑将自适应神经模糊推理系统(ANFIS)模型与进化算法(即哈里斯鹰优化算法(HHO)和算术优化算法(AOA))相结合,预测伊朗西北部沙赫尔恰伊河的洪水。使用的数据包括 2016 年和 2017 年的日降水量、蒸发量和径流量数据,其中 70% 的数据用于模型训练,其余数据用于测试模型。结果表明,虽然 ANFIS 模型在几个步骤中提供的数值误差较大,尤其是在具有最大值或最小值的步骤中,但使用 HHO 和 AOA 优化算法后,误差值显著减少。ANFIS-AOA 模型的输入情景包括前一至三天的流量,在测试数据中取得了最理想的结果,纳什-苏克里夫效率(NSE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)分别为 0.93、1.34 和 0.69。根据泰勒图,ANFIS-AOA 混合算法预测洪水值的性能高于其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Hydrology Research
Hydrology Research WATER RESOURCES-
CiteScore
5.00
自引率
7.40%
发文量
0
审稿时长
3.8 months
期刊介绍: Hydrology Research provides international coverage on all aspects of hydrology in its widest sense, and welcomes the submission of papers from across the subject. While emphasis is placed on studies of the hydrological cycle, the Journal also covers the physics and chemistry of water. Hydrology Research is intended to be a link between basic hydrological research and the practical application of scientific results within the broad field of water management.
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