Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)
Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)
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DOI：
10.1007/s11269-013-0361-9
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发表时间：
2013-07-01
影响因子：
4.3
通讯作者：
Jaafar, Othman
中科院分区：
文献类型：
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作者：
Akrami, Seyed Ahmad;El-Shafie, Ahmed;Jaafar, Othman
降雨是径流预测和水管理中最复杂有效的水文过程之一。自适应神经模糊推理系统（ANFIS）已广泛应用于各种非线性系统的建模，包括降雨预报。自适应神经模糊推理系统（ANFIS）结合人工神经网络（ANN）和模糊推理系统（FIS）的能力来解决各种各样的问题，特别是在降雨预测方面。本文在对传统的ANFIS结构进行反思的基础上，提出了一种改进的ANFIS结构（MANFlS），并在均方根误差（RMSE）、相关系数（R(2)）、均方根绝对误差（RMAE）、信噪比（SNR）和计算时代等方面提高了ANFIS技术的效率。改进的ANFIS （MANFIS）结构比传统的ANFIS结构简单，在建模非线性系统时具有几乎相同的性能。在本研究中，介绍了两种场景；在第一个场景中，仅将月降雨量作为输入，从时间(t)到时间（t-4）的不同时间延迟到常规ANFIS，第二个场景中使用改进的ANFIS来提高降雨预报效率。结果表明，基于修正ANFIS模型的降水预报精度较高；与传统的ANFIS模型相比，误差小，计算复杂度低（拟合参数总数和收敛周期）。
Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient (R (2)), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.