Remote sensing of water depths in shallow waters via artificial neural networks     

Özçelik Ceyhun  Arısoy Yalçın 《Estuarine, Coastal and Shelf Science》2010

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.  相似文献   

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model   总被引：10，自引：0，他引：10  

Biswajeet Pradhan  Ahmed M. Youssef  Renuganth Varathrajoo 《地球空间信息科学学报》2010,13(2):93-102

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.  相似文献   

Impact of artificial subsurface drainage on groundwater travel times and baseflow discharge in an agricultural watershed,Iowa (USA)     

Keith E. Schilling  Priyanka Jindal  Nandita B. Basu  Matthew J. Helmers 《水文研究》2012,26(20):3092-3100

Pollutant delivery through artificial subsurface drainage networks to streams is an important transport mechanism, yet the impact of drainage tiles on groundwater hydrology at the watershed scale has not been well documented. In this study, we developed a two‐dimensional, steady‐state groundwater flow model for a representative Iowa agricultural watershed to simulate the impact of tile drainage density and incision depth on groundwater travel times and proportion of baseflow contributed by tile drains. Varying tile drainage density from 0 to 0.0038 m^?1, while maintaining a constant tile incision depth at 1.2 m, resulted in the mean groundwater travel time to decrease exponentially from 40 years to 19 years and increased the tile contribution to baseflow from 0% to an upper bound of 37%. In contrast, varying tile depths from 0.3 to 2.7 m, while maintaining a constant tile drainage density of 0.0038 m^?1, caused mean travel times to decrease linearly from 22 to 18 years and increased the tile contribution to baseflow from 30% to 54% in a near‐linear manner. The decrease in the mean travel time was attributed to decrease in the saturated thickness of the aquifer with increasing drainage density and incision depth. Study results indicate that tile drainage affects fundamental watershed characteristics and should be taken into consideration when evaluating water and nitrate export from agricultural regions. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Modelling combined open channel flow by artificial neural networks     

Han‐Chung Yang  Fi‐John Chang 《水文研究》2005,19(18):3747-3762

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?     

《水文科学杂志》2013,58(1)

Abstract

Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.  相似文献   

Prediction of the sawing quality of Marmarit stones using the capability of artificial neural network     

Hassan Yarmohamadi Samani  Ali Reza Yarahmadi Bafghi 《国际地质力学数值与分析法杂志》2012,36(7):881-891

The sawing rate is one of the most significant and effective parameters in extracting building stones via diamond wire sawing. This parameter designates the capability of diamond wire sawing for sawing different stones; in addition, the parameter gives rise to economical considerations for quarry designers. In this study, the existent relations between stone geotechnical parameters and the sawing rate of stones via diamond wire sawing were analyzed using regression and correlation coefficient as well as the collected data from Marmarit stone quarries. Moreover, we estimated the sawing rate of Marmarit using the dimensional stone rock mass rating (DSRMR); upon comparison of the data obtained from DSRMR our pre‐collected data on quarries, we did not gain satisfactory results from DSRMR, hence we used artificial neural network (ANN). The results showed that the percentage of Silica, the coefficient of water absorption, the uniaxial compressive strength (UCS), and abrasive hardness are the proper parameters for creating the ANN. Discontinuities have the least effects possible on diamond wire sawing. Having given the training possibility of the ANN, and its ability to evaluate relations among input parameters, the ANN, which was being trained with Marmarit's traits, was an accurate network for estimating diamond wire sawing in Marmarit quarries, although it could not generalize this network for other stones such as Chini and Crystal. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

马氏珠母贝种苗规模化繁育新技术及工艺     

孙小真  刘志刚 《海洋科学》2010,34(10):62-67

对马氏珠母贝人工育苗换水、投附着基和饵料等关键环节进行了研究。结果表明:(1)不换水组的D形幼虫及壳顶幼虫的存活率,稚贝育成率以及D形幼虫、壳顶幼虫及稚贝壳长日生长率比换水组分别提高了15.3%、259.6%、186.5%、33.3%、34.2%、12.4%,且差异显著;(2)第1、2次投附着板组的稚贝壳长日生长率均比一次性投附着板组快,第3次投附着板组的壳长日生长率比其他所有组均慢,且差异均显著。多次投附着板组的同一批次稚贝均匀度均比一次性投附着板组好,且多次投附着板组比一次性投附着板组的稚贝育成率提高了32.5%,稚贝存活率提高了19.3%,采苗量提高了35%;(3)投喂虾塘水组稚贝存活率、育成率及壳长日生长率比投喂50%自溶酵母+50%小球藻组分别提高了28.1%、47.2%、35.9%,而投喂这两种不同饵料的稚贝阴干后的存活率差异不显著。研究表明,通过封闭不换水育苗、多次投附着板及投喂虾塘水中的生态饵料的方法可以高效地培育出健康的马氏珠母贝种苗。  相似文献   

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers     

Seyed Ahmad Mirbagheri  Taher Rajaee  Amir Alikhani 《水文科学杂志》2013,58(7):1175-1189

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.  相似文献   

Isotopes and geochemistry in a managed aquifer recharge scheme: a case study of fresh water injection at the Damascus University Campus,Syria     

Z. Kattan  N. Kadkoy  S. Nasser  M. Safadi  A. Hamed 《水文研究》2010,24(13):1791-1805

A combination of stable isotopes (¹⁸O and ²H) and hydrochemistry has been applied to investigate storage processes in relation to aquifer storage and recovery (ASR) of the shallow alluvial Quaternary aquifer in Damascus basin. The stored water, entirely taken from the Figeh springs during flood periods, was injected in a single well having a brackish groundwater. Water samples were collected from four observation wells drilled in the Damascus University Campus (DUC) site during a 3‐year period (2006–2008). The injectant water, which deviates in its chemical and isotopic signatures from that of the ambient groundwater, shows that the stored water plume remains within close proximity to the injection well (IW) (<≈ 100 m). Thus, only two wells (W13 and W14) located at a distance less than 80 m from the injection point were affected by this injection. The observation wells located at longer distances from the IW (≈145 m and ≈ 600 m for wells W15 and WHz, respectively) were completely unaffected by the injection. Although most of the chemical and isotopic parameters usefully reflected the mixing process that occurs between the injectant water and ambient groundwater, the stable isotope (¹⁸O) and chloride (Cl⁻) were the most sensitive parameters that quickly reflect this signature. Using a simple mass balance, the calculated proportion of injectant water reaching the well W13 was in the range of 50–90%. This proportion was even lower (30–55%) in the case of well W14. Although the drought event prevailing during this study did not much help to inject further amounts of water, higher than the injected volume (0·2416 M m³) and also not favourable to better evaluate the fate and subsurface hydrological processes, these findings offer encouragement to continue the ASR activities, as an alternative way for better management of water resources in this basin facing intensive problems. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

平坦地区GPS高程异常拟合方法研究     

赵云  曹先密 《测绘工程》2010,19(3):24-25,38

结合GPS测量和水准测量资料,用BP人工神经网络和RBF人工神经网络方法和二次多项式曲面拟合方法拟合高程异常,对平坦地区GPS高程异常拟合精度进行比较分析,得出有实用价值的结论。  相似文献