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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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A drift and pumpback experiment was conducted in a brackish water sandfill. The sandfill was reclaimed from the sea in the eastern part of Singapore and contains sands with low organic and clay/silt contents. The high salinity in the ground water precludes the use of chloride and bromide as tracers in such an environment, and a field experiment was conducted to assess the viability of using fluorescein as a tracer in brackish water aquifers. Nitrate was used as a second tracer to serve as a check. Initial laboratory studies showed that fluorescence was unaffected over the range of electrical conductivity and pH of the ground water. Results from the field experiment show that fluorescein appears to behave conservatively. 相似文献
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A thorough understanding of rainfall recharge processes and their controlling factors is essential for management of groundwater systems. This study investigates the effects of various meteorological and hydrogeological factors on the gross recharge percentages, the rainfall–recharge relationships and the recharge threshold values for unconfined sandy aquifers under an equatorial climate. Among the meteorological factors investigated, rainfall intensity was found to have the most significant impact on the gross recharge rate. The effects of potential evaporation rate, relative humidity and air temperature on the gross recharge percentage were significant when the vadose zone thickness is larger than 2·5 m. The recharge threshold values were found to depend strongly on the vadose zone thickness. The rainfall–recharge relationships could generally be well defined by a normal–log relationship. The rainfall–recharge relationships derived here are applicable to yield estimates of gross recharge percentages for unconfined sandy aquifers under an equatorial climate, using rainfall intensity and vadose zone thickness as input variables. In this study, a theory was developed and validated to provide physical explanations for the observations, based on the residence time of the percolated rainwater within the vadose zone. Among the soil hydraulic parameters tested, porosity and saturated hydraulic conductivity were found to have the most pronounced effects on the gross recharge percentage. Utilizing the sensitivity results and the theory derived, an approach was developed for extending the application of the derived rainfall–recharge relationships to other sand textures. The approach was found to be capable of producing rough and fast estimations of gross recharge percentage for other sand textures. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
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Groundwater recharge studies are essential for investigating the feasibility of using the reclaimed lands in Singapore for subsurface storage and recovery of water. Through time‐series and spectral analyses, net recharge percentages and stress‐filtering characteristics at the reclaimed land were found to depend strongly on the stress transfer velocity, which was a combined function of rainfall pattern and vadose zone thickness. Based on stress transfer velocity, a theory was established to provide logical explanations for the rainfall–recharge relationship, the observed stress‐filtering characteristics and the recharge percentage characteristics at the unconfined sandy aquifer. Although the reclaimed land site has a lithollogically homogeneous soil profile, a non‐uniform recharge pattern was observed to be influenced pronouncedly by the uneven density distribution of bush grasses. Under a bare soil condition, significantly lower recharge percentages were observed for areas under the influence of offshore tides. The unconfined sandy aquifer appears to dampen out the wave propagation of offshore tides rapidly within a short distance from the tidal source, though it has a fairly straight shoreline and is created from highly compacted sand fills. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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A new approach is demonstrated that permits a reliable estimate of specific yield using published values of the van Genuchten water retention parameters and effective grain sizes and the measured effective grain sizes of soil samples. The specific yield distribution of the soil texture was computed using the published values of the van Genuchten parameters. The specific yield values and the published values of effective grain sizes were then used to construct a specific yield–effective grain size curve, which estimates the ‘point’ specific yield of the soil samples. Applying the central limit theorem, the point specific yields could be transformed into an ‘areal’ specific yield for a study area. Compared with other commonly used approaches, the present procedure requires relatively low computational efforts and readily obtainable data. It is cost effective and does not depend on soil texture classification. More importantly, it incorporates the depth to water table and the variations in grain sizes inherent in natural soil conditions in the estimation. The approach developed was applied for estimating the specific yield of an unconfined sandy aquifer created by land reclamation in the equatorial region. The values obtained were compared with field measurements and the typical ranges of specific yield from the literature. Instead of a single estimate of the specific yield, the method yields a confidence interval with a high confidence level of 95% and with a narrower range than the typical ranges from the literature. In addition, the estimated values are close to the field measurements; hence, the procedure provides a cost‐effective alternative to field measurement. The applicability of the present approach could be extended to sites with heterogeneity in the horizontal direction. Nevertheless, the applicability of the present approach for layered soil profiles requires further evaluations. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Flood forecasting in large rivers with data-driven models 总被引：1，自引：1，他引：0
Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m. 相似文献
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An analytical procedure is presented for determining groundborne vibrations in buildings due to subway trains. The procedure involves a finite element idealization of the subway-soil-structure interaction problem, using an analytical model for the train loading spectrum at the tunnel invert. Both direct fixation and floating slab track support systems are considered. The train model is verified using the measurements of rail velocities. The proposed procedure is applied to the case of a four-storey podium block enclosing twin double-box subways within the confines of its ribbed wall foundations. The severity of velocity response levels of the building, in relation to vibration standards, is also considered. 相似文献
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The South China Sea is a large marginal sea surrounded by land masses and island chains, and characterized by complex bathymetry and irregular coastlines. An unstructured-grid SUNTANS model is employed to perform depth-averaged simulations of the circulation in the South China Sea. The model is tidally forced at the open ocean boundaries using the eight main tidal constituents as derived from the OSU Tidal Prediction Software. The model simulations are performed for the year 2005 using a time step of 60 s. The model reproduces the spring-neap and diurnal and semidiurnal variability in the observed data. Skill assessment of the model is performed by comparing model-predicted surface elevations with observations. For stations located in the central region of the South China Sea, the root mean squared errors (RMSE) are less than 10 % and the Pearson’s correlation coefficient (r) is as high as 0.9. The simulations show that the quality of the model prediction is dependent on the horizontal grid resolution, coastline accuracy, and boundary locations. The maximum RMSE errors and minimum correlation coefficients occur at Kaohsiung (located in northern South China Sea off Taiwan coast) and Tioman (located in southern South China Sea off Malaysia coast). This may be explained with spectral analysis of sea level residuals and winds, which reveal dynamics at Kaohsiung and Tioman are strongly influenced by the seasonal monsoon winds. Our model demonstrates the importance of tidally driven circulation in the central region of the South China Sea. 相似文献