Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study     

Mamdouh A. Antar  Ibrahim Elassiouti  Mohamed N. Allam 《水文研究》2006,20(5):1201-1216

A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km²) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor   总被引：3，自引：0，他引：3  

Nachiketa Acharya  Surajit Chattopadhyay  Makarand A. Kulkarni  Uma C. Mohanty 《Acta Geophysica》2012,60(1):260-279

A relationship between summer monsoon rainfall and sea surface temperature anomalies was investigated with the aim of predicting the monthly scale rainfall during the summer monsoon period over a section (80°–90°E, 14°–24°N) of eastern India that depends heavily upon the rainfall during the summer monsoon months for its agricultural practices. The association between area-averaged rainfall of June over the study zone and global sea surface temperature (SST) anomalies for the period 1982–2008 was examined and the variability of rainfall in monthly scale was calculated. With a view to significant variability in the rainfall in the monthly scale, it was decided to implement the artificial neural network (ANN) for forecasting the monthly scale rainfall using the SST anomalies as a predictor. Finally, the potential of ANN in this prediction has been assessed.  相似文献   

A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change     

Ping-Cheng Hsieh  Wei-An Tong 《水文科学杂志》2013,58(14):1793-1802

ABSTRACT

A forecasting model is developed using a hybrid approach of artificial neural network (ANN) and multiple regression analysis (MRA) to predict the total typhoon rainfall and groundwater-level change in the Zhuoshui River basin. We used information from the raingauge stations in eastern Taiwan and open source typhoon data to build the ANN model for forecasting the total rainfall and the groundwater level during a typhoon event; then we revised the predictive values using MRA. As a result, the average accuracy improved up to 80% when the hybrid model of ANN and MRA was applied, even where insufficient data were available for model training. The outcome of this research can be applied to forecasts of total rainfall and groundwater-level change before a typhoon event reaches the Zhuoshui River basin once the typhoon has made landfall on the east coast of Taiwan.  相似文献   

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models          下载免费PDF全文

Qiang Dai  Miguel A. Rico‐Ramirez  Dawei Han  Tanvir Islam  Sara Liguori 《水文研究》2015,29(1):66-79

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

A recurrent support vector regression model in rainfall forecasting     

Ping‐Feng Pai  Wei‐Chiang Hong 《水文研究》2007,21(6):819-827

To minimize potential loss of life and property caused by rainfall during typhoon seasons, precise rainfall forecasts have been one of the key subjects in hydrological research. However, rainfall forecast is made difficult by some very complicated and unforeseen physical factors associated with rainfall. Recently, support vector regression (SVR) models and recurrent SVR (RSVR) models have been successfully employed to solve time‐series problems in some fields. Nevertheless, the use of RSVR models in rainfall forecasting has not been investigated widely. This study attempts to improve the forecasting accuracy of rainfall by taking advantage of the unique strength of the SVR model, genetic algorithms, and the recurrent network architecture. The performance of genetic algorithms with different mutation rates and crossover rates in SVR parameter selection is examined. Simulation results identify the RSVR with genetic algorithms model as being an effective means of forecasting rainfall amount. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Prediction of rainfall-triggered landslides: a test of the Antecedent Water Status Model     

Michael J. Crozier 《地球表面变化过程与地形》1999,24(9):825-833

A rainfall-based landslide-triggering model, developed from previous landslide episodes in Wellington City, New Zealand, is tested for its ability to provide a 24-hour forecast of landslide occurrence. The model, referred to as the Antecedent Water Status Model, calculates an index of soil water, by running a daily water balance and applying a soil drainage factor to excess precipitation, over the preceding ten days. Together with the daily rainfall input, the soil water status has been used empirically to identify a threshold condition for landslide triggering. The prediction process provides a daily update of the soil water status and thereby the amount of rainfall required on the following day to equal or exceed the triggering threshold. The probability that this triggering rainfall will occur is then determined from the frequency/magnitude distribution of the local rainfall record. The model produces a satisfactory level of prediction, particularly for periods of concentrated landslide activity. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

Forecasting the summer rainfall in North China using the year-to-year increment approach   总被引：5，自引：0，他引：5  

Ke Fan  MeiJing Lin  YuZhong Gao 《中国科学D辑(英文版)》2009,52(4):532-539

A new approach to forecasting the year-to-year increment of rainfall in North China in July–August (JA) is proposed. DY is defined as the difference of a variable between the current year and the preceding year (year-to-year increment). NR denotes the seasonal mean precipitation rate over North China in JA. After analyzing the atmospheric circulation anomalies associated with the DY of NR, five key predictors for the DY of NR have been identified. The prediction model for the DY of NR is established by using multi-linear regression method and the NR is obtained (the current forecasted DY of NR added to the preceding observed NR). The prediction model shows a high correlation coefficient (0.8) between the simulated and the observed DY of NR throughout period 1965–1999, with an average relative root mean square error of 19% for the percentage of precipitation rate anomaly over North China. The prediction model makes a hindcast for 2000–2007, with an average relative root mean square error of 21% for the percentage of precipitation rate anomaly over North China. The model reproduces the downward trend of the percentage of precipitation rate anomaly over North China during 1965–2006. Because the current operational prediction models of the summer precipitation have average forecast scores of 60%–70%, it has been more difficult to forecast the summer rainfall over North China. Thus this new approach for predicting the year-to-year increment of the summer precipitation (and hence the summer precipitation itself) has the potential to significantly improve operational forecasting skill for summer precipitation. Supported by National Basic Research Program of China (Grant No. 2009CB421406), National Natural Science Foundation of China (Grant Nos. 40631005, 40775049) and Excellent Ph. D Dissertation in Chinese Academy of Sciences  相似文献   

An Objective Approach for Prediction of Daily Summer Monsoon Rainfall over Orissa (India) due to Interaction of Mesoscale and Large-scale Synoptic Systems     

M. Mohapatra  U. C. Mohanty 《Pure and Applied Geophysics》2007,164(8-9):1683-1698

Orissa State, a meteorological subdivision of India, lies on the east coast of India close to north Bay of Bengal and to the south of the normal position of the monsoon trough. The monsoon disturbances such as depressions and cyclonic storms mostly develop to the north of 15° N over the Bay of Bengal and move along the monsoon trough. As Orissa lies in the southwest sector of such disturbances, it experiences very heavy rainfall due to the interaction of these systems with mesoscale convection sometimes leading to flood. The orography due to the Eastern Ghat and other hill peaks in Orissa and environs play a significant role in this interaction. The objective of this study is to develop an objective statistical model to predict the occurrence and quantity of precipitation during the next 24 hours over specific locations of Orissa, due to monsoon disturbances over north Bay and adjoining west central Bay of Bengal based on observations to up 0300 UTC of the day. A probability of precipitation (PoP) model has been developed by applying forward stepwise regression with available surface and upper air meteorological parameters observed in and around Orissa in association with monsoon disturbances during the summer monsoon season (June-September). The PoP forecast has been converted into the deterministic occurrence/non-occurrence of precipitation forecast using the critical value of PoP. The parameters selected through stepwise regression have been considered to develop quantitative precipitation forecast (QPF) model using multiple discriminant analysis (MDA) for categorical prediction of precipitation in different ranges such as 0.1–10, 11–25, 26–50, 51–100 and >100 mm if the occurrence of precipitation is predicted by PoP model. All the above models have been developed based on data of summer monsoon seasons of 1980–1994, and data during 1995–1998 have been used for testing the skill of the models. Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 96% and 88%, respectively for developmental and independent data. The skill of the QPF model, though relatively less, is reasonable for lower ranges of precipitation. The skill of the model is limited for higher ranges of precipitation. accepted September 2006  相似文献   

Uncertainties in real‐time flood forecasting with neural networks     

Dawei Han  Terence Kwong  Simon Li 《水文研究》2007,21(2):223-228

Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Identification of best predictors for forecasting seasonal rainfall and runoff in Australia   总被引：2，自引：0，他引：2  

Dewi G. C. Kirono  Francis H. S. Chiew  David M. Kent 《水文研究》2010,24(10):1237-1247

The first step towards developing a reliable seasonal runoff forecast is identifying the key predictors that drive rainfall and runoff. This paper investigates the lag relationships between rainfall across Australia and runoff across southeast Australia versus 12 atmospheric‐oceanic predictors, and how the relationships change over time. The analysis of rainfall data indicates that the relationship is greatest in spring and summer in northeast Australia and in spring in southeast Australia. The best predictors for spring rainfall in eastern Australia are NINO4 [sea surface temperature (SST) in western Pacific] and thermocline (20 °C isotherm of the Pacific) and those for summer rainfall in northeast Australia are NINO4 and Southern Oscillation Index (SOI) (pressure difference between Tahiti and Darwin). The relationship in northern Australia is greatest in spring and autumn with NINO4 being the best predictor. In western Australia, the relationship is significant in summer, where SST2 (SST over the Indian Ocean) and II (SST over the Indonesian region) is the best predictor in the southwest and northwest, respectively. The analysis of runoff across southeast Australia indicates that the runoff predictability in the southern parts is greatest in winter and spring, with antecedent runoff being the best predictor. The relationship between spring runoff and NINO4, thermocline and SOI is also relatively high and can be used together with antecedent runoff to forecast spring runoff. In the northern parts of southeast Australia, the atmospheric‐oceanic variables are better predictors of runoff than antecedent runoff, and have significant correlation with winter, spring and summer runoff. For longer lead times, the runoff serial correlation is reduced, especially over the northern parts, and the atmospheric‐oceanic variables are likely to be better predictors for forecasting runoff. The correlations between runoff versus the predictors vary with time, and this has implications for the development of forecast relationship that assumes stationarity in the historical data. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

Identification of physical processes inherent in artificial neural network rainfall runoff models     

Ashu Jain  K. P. Sudheer  Sanaga Srinivasulu 《水文研究》2004,18(3):571-581

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

Prediction of Chinese Loess Plateau summer rainfall using Pacific Ocean spring sea surface temperature     

H. Yasuda  R. Berndtsson  T. Saito  H. Anyoji  X. Zhang 《水文研究》2009,23(5):719-729

The Loess Plateau in China constitutes an important source area for both water and sediments to the Yellow River. Thus, improved prediction techniques of rainfall may lead to better estimation of discharge and sediment content for the Yellow River. Consequently, the objective of this study was to establish better links between rainfall of the Loess Plateau in China and sea surface temperature (SST) in the Pacific Ocean. Results showed that there is a strong lagged correlation between and SST and rainfall. The SST for Micronesia and areas south of the Aleutian Islands showed significant correlations (s.f. < 0·001; 99·9%) with rainfall over the dryer region of the Loess Plateau for a lag of 4 to 6 months. The SST over the equator on the east Pacific Ocean also showed significant negative correlation with rainfall. Low and middle latitude areas (S10–20° and around 30° ) of the south‐east Pacific Ocean displayed significant positive and negative correlation with rainfall on the semiarid Loess Plateau. The differenced SST values (positive SST minus negative SST) increased these correlations with rainfall. An artificial neural network (ANN) model was used to predict summer rainfall from the differenced SST during the spring period. The correlation between predicted and observed monthly rainfall was in general larger than 0·7. This indicates that major annual rainfall (during summer season) can be predicted with good accuracy using the suggested approach. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

Short‐term inflow forecasting using an artificial neural network model     

Z. X. Xu  J. Y. Li 《水文研究》2002,16(12):2423-2439

The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

A systematic approach to the input determination for neural network rainfall–runoff models     

Gwo‐Fong Lin  Guo‐Rong Chen 《水文研究》2008,22(14):2524-2530

Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs are removed. Then an adequate ANN model, which only includes highly relevant inputs, is constructed. Unlike the trial‐and‐error procedure, the proposed approach is more systematic and avoids unnecessary trials. To demonstrate the effectiveness of the proposed approach, an application to actual typhoon events is presented. The results show that the proposed ANN model, which is constructed by the proposed approach, has advantages over those obtained by the trial‐and‐error procedure. The proposed ANN model has a simpler architecture, needs less training time, and performs better. The proposed ANN model is recommended as an alternative to existing rainfall–runoff ANN models. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Wavelet analysis of rainfall variation in the Hebei Plain   总被引：5，自引：0，他引：5  

XU Yueqing  LI Shuangcheng & CAI Yunlong Key Laboratory for Earth Surface Process  Ministry of Education  Department of Resources  Environmental Geosciences  College of Environmental Sciences  Peking University  Beijing  China 《中国科学D辑(英文版)》2005,48(12):2241-2250

Rainfall is an important climate factor, which has significant impacts on agricultural production and na-tional economic development[1]. Being part of the North China Plain, the Hebei Plain is an agricultural region. Under the continental monsoon climate, it is cold and dry in winter, hot and rainy in summer, and its variable rainfall is concentrated in summer. Droughts and floods occur frequently and impose sig-nificant impacts on agricultural production. Studies on the characteristics and …  相似文献   

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models     

Shan He  Srivatsan V. Raghavan  Ngoc Son Nguyen  Shie‐Yui Liong 《水文研究》2013,27(11):1560-1571

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

An evaluation of numerical weather prediction based rainfall forecasts     

Mahshid Shahrban  Jeffrey P. Walker  Q. J. Wang  Alan Seed  Peter Steinle 《水文科学杂志》2013,58(15):2704-2717

ABSTRACT

Assessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on the daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the numerical weather prediction (NWP) outputs.