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1.
Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan 
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下载免费PDF全文 Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Milad Janalipour 《International Journal of Digital Earth》2018,11(12):1241-1268
Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings. 相似文献
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Babak Mohammadi Nguyen Thi Thuy Linh Ali Najah Ahmed Jana Vojteková Yiqing Guan 《水文科学杂志》2020,65(10):1738-1751
ABSTRACT Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R2 = 0.88; NS = 0.88; RMSE = 142.30 (m3/s); MAE = 88.94 (m3/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R2 = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m3/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide. 相似文献
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自适应神经模糊推理系统(ANFIS)在水文模型综合中的应用 总被引：1，自引：0，他引：1
由于目前已有很多比较成熟的流域水文模型,因此我们可以选用几个流域水文模型进行并行运算,来同时模拟流域降雨—径流关系。在相同的降雨输入情况下,不同模型得到的模拟流量必然会有所不同,模型效率系数和模拟精度也会不同。因此,如何将不同模型的模拟结果进行综合以进一步提高流量模拟精度是一个关键问题。本文选用自适应神经模糊推理系统(ANFIS)作为水文模型综合平台,以牧马河流域为试验区域,对两个并行运算水文模型(三水源新安江模型和总径流响应模型)的结果进行综合处理,得到了更稳健的流量模拟结果,大大提高了模型效率和模拟精度。该方法值得在实践中借鉴。 相似文献
6.
Based on the adaptive network fuzzy inference system (ANFIS), methods to filter out the noise of impact factors from the main signal are discussed. Focusing on the abnormal weather conditions in 2010, we use the delay-relevant method to analyze the five members of the summer monsoon system that had the largest effect on the subtropical high anomalies from the observational data. ANFIS is suitable for research and simulation of subtropical highs that are difficult to describe accurately with dynamics, allowing the effect of five factors on the subtropical high anomalies to be examined. Our results show that the Mascarene cold high, the Indian monsoon latent heat flux, and the South China Sea monsoon trough had the largest effect on the subtropical high anomalies. Diagnostic analysis, with genetic algorithms (GA) and dynamical reconstruction theory, reconstructed the nonlinear dynamical model of the subtropical high and its main factors objectively and accurately from the sequence of observations in 2010. Furthermore, a dynamically extended forecast experiment is performed. The forecasts for the subtropical high area index, the Mascarene cold high index, the Indian monsoon latent heat flux, and the South China Sea monsoon trough index all show a strong short-term effect over less than 25 days. The forecasting trend is accurate, and the error rate is no more than 7%. Our results provide new insight and methods for research on the association between the western Pacific subtropical high and the East Asian summer monsoon system, and for the prediction of the western Pacific subtropical high index. 相似文献
7.
Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting. 相似文献
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Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series
《水文科学杂志》2013,58(4):588-598
Abstract The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS. 相似文献
10.
土地利用变化在空间维和时间维上是一个渐进的、不确定的复杂过程,而模糊理论正是解决不确定性现象的一种合适的方法,所以尝试运用模糊推理理论对土地利用变化进行深入的探讨.以江苏省南通市崇川区为研究区,建立了基于自适应神经模糊推理系统(ANFIS)的土地利用变化模糊推理模型.通过利用ANFIS训练获得模型的隶属函数及参数.并运用该模型对研究区进行土地利用变化的模拟和预测.研究结果表明,通过利用ANFIS建立的模型,基本上可以模拟研究区复杂而不确定的土地利用变化过程,同时ANFIS可以有效地简化模糊推理模型结构,使得模型更具灵活性.因此,为土地利用变化模拟提供了另一种可行的解决思路. 相似文献