An automatic tool for reconstructing monthly time-series of hydro-climatic variables at ungauged basins

Integrative information models for filling/reconstructing hydro-climatic time-series are required for a variety of practical applications. A GIS-based model for a rapid and reliable assessment of monthly time-series of several key hydro-climatic variables at the basin scale, is here developed as plug-in and applied to the entire region of Sicily (Italy). The plug-in, once the desired basin outlet section and time-window are selected, uses appropriate spatial techniques and algorithms to identify its drainage area and estimate the corresponding mean areal rainfall and temperatures time-series. A recent regional regressive rainfall-runoff model is successively applied for the assessment of the runoff time-series. Finally, a consolidated temperature-based method is applied to estimate monthly potential evapotranspiration time-series, while, actual evapotranspiration and soil moisture storage time-series are derived through a classical water balance model. The tool, supported by a preliminarily developed database, includes automatic procedures for data retrieving and processing and a user friendly interface.     

Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data

The rainfall runoff (R-R) process was studied for two small sub-basins having different sizes in a mountainous catchment of Tono area Japan. The runoff and other meterological data have been collected in this catchment for the last 14 years. The major objective of this study was to construct numerical models for these sub-basins to predict runoff after 1/2 and 1 h. The effects of season and the size of the catchment on R-R process were also investigated. The hydrogeological conditions of the catchment were studied prior to the analyses. The data obtained for summer (rainy) and winter (dry) seasons were treated separately in order to study the seasonal effects on the model development. The back propagation artificial neural network technique (BPANN) and the multivariate autoregressive and moving average models (ARMA) were adopted for the analysis. It was found that for very small catchments the seasonal effects are dominant and therefore separate models should be developed for each season to obtain better forecasting estimates. It was also found that the predictions by BPANN models were better than multivariate ARMA models for intense rains having complex R-R relationships in summer. On the other hand, both the modelling techniques yielded almost similar results for smaller rains in winter. It was also found clearly that the accuracy of prediction decreased with the increase of the time period for prediction.     

一种基于FCM的医学图像检索方法与实现

针对医学内窥镜图像,提出两种基于模糊C-均值聚类(FCM)的特征融合算法:融合颜色相关图和图像纹理特征算法以及融合颜色直方图和颜色相关图算法。据此,实现了一个图像检索的原型系统,依据所设计的评价实验,并对实验结果进行了比较和分析。实验表明,基于FCM的融合颜色相关图和纹理特征的特征融合算法,在基于内容的医学内窥镜图像检索中,具有相对较好的检索效果。     

Wavelet correlogram: A new approach for image indexing and retrieval

In this paper, a new algorithm for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution image decomposition and color correlation histogram. According to the new algorithm, wavelet coefficients of the image are computed first using a directional wavelet transform such as Gabor wavelets. A quantization step is then applied before computing one-directional autocorrelograms of the wavelet coefficients. Finally, index vectors are constructed using these one-directional wavelet correlograms. The retrieval results obtained by application of our new method on a 1000 image database demonstrated a significant improvement in effectiveness and efficiency compared to the indexing and retrieval methods based on image color correlogram or wavelet transform.     

Influence of Loss Model on Design Discharge of Homogeneous Plane

By applying the kinematic wave method to a homogeneous, rectangular overland plane, the influence of the constant and proportional-loss models on the design discharge are examined. The examination shows that with the use of the proportional-loss model, there is no partial-area effect and the design discharge is governed by the full-area contribution. On the other hand, with the use of the constant-loss model, there is no partial-area effect if the loss rate is small. For larger loss rates, there is a partial-area effect; and for this case, the design is complicated, since there is a need to search for the critical storm that governs the partial-area effect. The characteristic of the critical storm is that its rate of decrease in rainfall intensity with duration equals the corresponding rate of increase in the contributing area. The preceding findings are consistent with the design concept in the rational method that uses the proportional-loss model, and the results from earlier studies that used the constant-loss model.     

Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States

We show useful seasonal deterministic and probabilistic prediction skill of streamflow and nutrient loading over watersheds in the Southeastern United States (SEUS) for the winter and spring seasons. The study accounts for forecast uncertainties stemming from the meteorological forcing and hydrological model uncertainty. Multi-model estimation from three hydrological models, each forced with an ensemble of forcing derived by matching observed analogues of forecasted quartile rainfall anomalies from a seasonal climate forecast is used. The attained useful hydrological prediction skill is despite the climate model overestimating rainfall by over 23% over these SEUS watersheds in December–May period. The prediction skill in the month of April and May is deteriorated as compared to the period from December–March (zero lead forecast). A nutrient streamflow rating curve is developed using a log linear tool for this purpose. The skill in the prediction of seasonal nutrient loading is identical to the skill of seasonal streamflow forecast.     

DDRM模型在渠江流域的应用研究

为了进一步探究分布式水文模型(DDRM)的适用性,基于渠江流域的数字高程模型(DEM),提取了研究区域的边界、河网水系,计算了研究区域的地形指数;然后采用基于DEM的DDRM模型进行流域降雨-径流过程模拟,并用确定性系数、洪峰相对误差、峰现时间误差等对DDRM模型的模拟精度进行了评价;同时,对比分析了不同DEM栅格分辨率(1 km和2 km)对模型模拟精度的影响。结果表明:基于上述2种分辨率的DDRM模型在渠江流域降雨-径流模拟中均取得了较好的模拟效果,其中基于1 km栅格的DDRM模拟精度略优于2 km栅格。DDRM模型结构简单,参数较少、物理过程明确,而且能够模拟流域土壤含水量和径流量的空间分布,可为缺资料地区水文模拟提供一种新的方法。     

基于二进制颜色相关矩阵的图像检索方法

提出一种基于二进制颜色相关矩阵的图像检索方法BMCCM。该方法利用RGB分量颜色不变量模型对原始图像进行量化,计算变换后图像的颜色相关矩阵,对相关矩阵进行二值化,并利用Jaccard系数衡量图像特征间的相似度,完成图像的检索。实验结果表明,BMCCM的检索准确度及效率明显高于颜色直方图和颜色(自)相关图。     

A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events

Accurate rainfall-runoff modeling during typhoon events is an essential task for natural disaster reduction. In this study, a novel hybrid model which integrates the outputs of physically based hydrologic modeling system into support vector machine is developed to predict hourly runoff discharges in Chishan Creek basin in southern Taiwan. Seven storms (with a total of 1200 data sets) are used for model calibration (training) and validation. Six statistical indices (mean absolute error, root mean square error, correlation coefficient, error of time to peak discharge, error of peak discharge, and coefficient of efficiency) are employed to assess prediction performance. Overall, superiority of the present approach especially for a longer (6-h) lead time prediction is revealed through a systematic comparison among three individual methods (i.e., the physically based hydrologic model, artificial neural network, and support vector machine) as well as their two hybrid combinations. Besides, our analysis and in-depth discussions further clarify the roles of physically based and data-driven components in the proposed framework.