Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series     

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   

Adaptive Neuro-Fuzzy Inference System for drought forecasting   总被引：3，自引：2，他引：1  

Ulker Guner Bacanli  Mahmut Firat  Fatih Dikbas 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(8):1143-1154

Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.  相似文献   

Meteorological drought analysis using data-driven models for the Lakes District,Turkey     

《水文科学杂志》2013,58(6):1114-1124

Abstract

Droughts may be classified as meteorological, hydrological or agricultural. When meteorological drought appears in a region, agricultural and hydrological droughts follow. In this study, the standardized precipitation index (SPI) was applied for meteorological drought analysis at nine stations located around the Lakes District, Turkey. Analyses were performed on 3-, 6-, 9- and 12-month-long data sets. The SPI drought classifications were modelled by Adaptive Neural-Based Fuzzy Inference System (ANFIS) and Fuzzy Logic, which has the advantage that, in contrast to most of the time series modelling techniques, it does not require the model structure to be known a priori. Comparison of the observed values and the modelling results shows a better agreement with SPI-12 and ANFIS models than with fuzzy logic models.  相似文献   

Multi-models for SPI drought forecasting in the north of Haihe River Basin,China     

Yuhu?Zhang  Weiwei?LiEmail author  Qiuhua?Chen  Xiao?Pu  Liu?Xiang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2471-2481

Drought is one of the most devastating climate disasters. Hence, drought forecasting plays an important role in mitigating some of the adverse effects of drought. Data-driven models are widely used for drought forecasting such as ARIMA model, artificial neural network (ANN) model, wavelet neural network (WANN) model, support vector regression model, grey model and so on. Three data-driven models (ARIMA model; ANN model; WANN model) are used in this study for drought forecasting based on standard precipitation index of two time scales (SPI; SPI-6 and SPI-12). The optimal data-driven model and time scale of SPI are then selected for effective drought forecasting in the North of Haihe River Basin. The effectiveness of the three data-models is compared by Kolmogorov–Smirnov (K–S) test, Kendall rank correlation, and the correlation coefficients (R²). The forecast results shows that the WANN model is more suitable and effective for forecasting SPI-6 and SPI-12 values in the north of Haihe River Basin.  相似文献   

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran     

Parveen Sihag  Vijay P. Singh  Anastasia Angelaki  Vinod Kumar  Alireza Sepahvand  Evangelia Golia 《水文科学杂志》2013,58(13):1647-1658

ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.  相似文献   

Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt     

《水文科学杂志》2013,58(6)

Abstract

Abstract The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and down-stream station sediment data, separately, and the second part focuses on the estimation of downstream suspended sediment data by using data from both stations. In each case, the MLP test results are compared to those of generalized regression neural networks (GRNN), radial basis function (RBF) and multi-linear regression (MLR) for the best-input combinations. Based on the comparisons, it was found that the MLP generally gives better suspended sediment concentration estimates than the other neural network techniques and the conventional statistical method (MLR). However, for the estimation of maximum sediment peak, the RBF was mostly found to be better than the MLP and the other techniques. The results also indicate that the RBF and GRNN may provide better performance than the MLP in the estimation of the total sediment load.  相似文献   

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique     

Nastaran Chitsaz  Ali Azarnivand  Shahab Araghinejad 《水文科学杂志》2013,58(12):2164-2178

ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.  相似文献   

Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River,Iran   总被引：3，自引：2，他引：1  

Rahim Barzegar  Jan Adamowski  Asghar Asghari Moghaddam 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(7):1797-1819

The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels of northwest Iran’s Aji-Chay River was assessed. The models were calibrated, validated and tested using different subsets of monthly records (October 1983 to September 2011) of individual solute (Ca²⁺, Mg²⁺, Na⁺, SO₄ ^2? and Cl^?) concentrations (input parameters, meq L^?1), and electrical conductivity-based salinity levels (output parameter, µS cm^?1), collected by the East Azarbaijan regional water authority. Based on the statistical criteria of coefficient of determination (R²), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (NSC) and threshold statistics (TS) the ANFIS model was found to outperform the ANN model. To develop coupled wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies, Symlet or Haar mother wavelets of different lengths (order), each implemented at three levels. To predict salinity input parameter series were used as input variables in different wavelet order/level-AI model combinations. Hybrid wavelet-ANFIS (R² = 0.9967, NRMSE = 2.9 × 10^?5 and NSC = 0.9951) and wavelet-ANN (R² = 0.996, NRMSE = 3.77 × 10^?5 and NSC = 0.9946) models implementing the db4 mother wavelet decomposition outperformed the ANFIS (R² = 0.9954, NRMSE = 3.77 × 10^?5 and NSC = 0.9914) and ANN (R² = 0.9936, NRMSE = 3.99 × 10^?5 and NSC = 0.9903) models.  相似文献   

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection     

Si?Chen  Ji?Yae?Shin  Tae-Woong?KimEmail authorView author&#;s OrcID profile 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(5):1061-1076

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.  相似文献   

Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed   总被引：1，自引：0，他引：1  

E. Mutlu  I. Chaubey  H. Hexmoor  S. G. Bajwa 《水文研究》2008,22(26):5097-5106

This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R² and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R² values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R² values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

Impacts of climate variability and change on drought characteristics in the Niger River Basin,West Africa   总被引：1，自引：0，他引：1  

Philip?G.?OguntundeEmail author  Gunnar?Lischeid  Babatunde?J.?Abiodun 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(4):1017-1034

West Africa has been afflicted by droughts since the declining rains of the 1970s. Therefore, this study examines the characteristics of drought over the Niger River Basin (NRB), investigates the influence of the drought on the river flow, and projects the impacts of future climate change on drought. A combination of observation data and regional climate simulations of past (1986–2005) and future climates (2046–2065 and 2081–2100) were analyzed. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were used to characterize drought while the standardized runoff index (SRI) was used to quantify river flow. Results of the study show that the historical pattern of drought is consistent with previous studies over the Basin and most part of West Africa. RCA4 ensemble gives realistic simulations of the climatology of the Basin in the past climate. Generally, an increase in drought intensity and frequency are projected over NRB. The coupling between SRI and drought indices was very strong (P < 0.05). The dominant peaks can be classified into three distinct drought cycles with periods 1–2, 2–4, 4–8 years. These cycles may be associated with Quasi-Biennial Oscillation (QBO) and El-Nino Southern Oscillation (ENSO). River flow was highly sensitive to precipitation in the NRB and a 1–3 month lead time was found between drought indices and SRI. Under RCP4.5, changes in the SPEI drought frequency range from 1.8 (2046–2065) to 2.4 (2081–2100) month year^?1 while under RCP8.5, the change ranges from 2.2 (2046–2065) to 3.0 month year^?1 (2081–2100). Niger Middle sub-basin is likely to be mostly impacted in the future while the Upper Niger was projected to be least impacted. Results of this study may guide policymakers to evolve strategies to facilitate vulnerability assessment and adaptive capacity of the basin in order to minimize the negative impacts of climate change.  相似文献   

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices     

Deepti Joshi  Andre St-Hilaire  Taha B. M. J. Ouarda  Anik Daigle  Nathalie Thiemonge 《水文科学杂志》2013,58(11):1996-2010

ABSTRACT

This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.