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1.
Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.  相似文献   

2.
Because glacial melting provides a significant amount of surface water resources, especially in cold arid regions, it is critical that effective methods be developed for predicting their behavior. Glacier runoff differs from other types of stream flows, being characterized by large diurnal fluctuations, with maximum discharge during the summer months. Moreover, the size and remoteness of glaciers makes them difficult to study directly. Hence, developing effective modeling techniques is our best hope for understanding and predicting glacial melting phenomena. In the past, physics-based models have been used with some success. In this study, conducted in 2003 and 2004 on the Keqikaer Glacier on the south slope of Mt. Tuomuer, however, we used the newer artificial neural networks (ANNs) modeling technique. As the input nerve cell, we used the hourly wind speed, precipitation, air temperature, radiation balance, and ground temperature; the output nerve cell was the diurnal runoff at the glacial terminus. We then analyzed the simulated results under different scenarios by varying the input-nerve-cell parameters. It was found that ANN can simulate the process of glacier meltwater runoff successfully when basic parameters such as air temperature, precipitation and radiation balance are few. The results indicate that ANN can simulate the process of glacial meltwater runoff quite well, and that meteorological variables could in fact be used successfully to simulate glacier meltwater runoff using the ANN method.  相似文献   

3.
This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.  相似文献   

4.
Complex-valued random fields represent a natural extension of real-valued random fields and can be useful for modeling vectorial data in two dimensions (i.e., a wind field). In such a case, some theoretical issues arise concerning generating and fitting complex covariance functions to be used for prediction purposes. In this paper, some general aspects and properties of complex-valued random fields are summarized and a procedure to fit complex stationary covariance functions is proposed. A case study for analyzing wind speed data is presented.  相似文献   

5.
Both statistical methods and artificial neural network (ANN) have been used for lithology or facies clustering. ANN, in particular, has increasingly gained popularity for clustering of categorical variables as well as for predictions of continuous variables. In this article, we discuss several counter examples that show deficiencies of these techniques when used for automatic lithofacies clustering. Our examples show that the lithofacies clustered by ANN alone or ANN in combination with principal component analysis (PCA), as commonly used, are highly inconsistent with the benchmark charts based on laboratory results. We propose several techniques to overcome these problems and improve the clustering of lithofacies, including (1) classification of lithofacies using the minor or intermediate principal component(s), (2) rotation of a principal component before using ANN for clustering, (3) cascading two or more PCAs and ANNs for clustering lithofacies or electrofacies, and (4) classifying lithofacies with demarcated stratigraphic reference classes.  相似文献   

6.
This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (γdmax) and optimum water content (ωopt). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.  相似文献   

7.
In this study, retrieval of temperature and humidity profiles of atmosphere from INSAT 3D-observed radiances has been accomplished. As the first step, a fast forward radiative transfer model using an Artificial neural network has been developed and it was proven to be highly effective, giving a correlation coefficient of 0.97. In order to develop this, a diverse set of physics-based clear sky profiles of pressure (P), temperature (T) and specific humidity (q) has been developed. The developed database was further used for geophysical retrieval experiments in two different frameworks, namely, an ANN and Bayesian estimation. The neural network retrievals were performed for three different cases, viz., temperature only retrieval, humidity only retrieval and combined retrieval. The temperature/humidity only ANN retrievals were found superior to combined retrieval using an ANN. Furthermore, Bayesian estimation showed superior results when compared with the combined ANN retrievals.  相似文献   

8.
Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.  相似文献   

9.
This study presents an integrated method for the estimation and analysis of potential wind-energy resources in Cyprus, which is applied at selected sites on the western side of the island. Firstly, a statistical analysis of wind speed and direction data was conducted at six meteorological stations in western Cyprus, establishing daily, monthly and annual variations of wind speed. Also examined were the Weibull distributions of the wind at each site. These wind statistics serve as the basis for estimating corrected statistical distributions over the extended study areas, which were calculated using the Wind Atlas Analysis and Application Program (WAsP) that modifies wind flow estimates based on local topographic effects. As a result, a geographic and wind-resource database was formulated around each station. Aggregation of this data using statistical weighting methods allows the extrapolation of observed results and the visualization for selected hours of the day over the western part of Cyprus. The results indicate the strong influence of the sea-breeze on the island’s wind potential, and identify a number of areas of higher wind-energy potential suitable for wind-resource exploitation. It is hoped that both the methodology applied and results obtained can be further used by potential investors and wind-energy developers.  相似文献   

10.
This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R2). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions.  相似文献   

11.
A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, EV).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.  相似文献   


12.
李林  李卫林  王振宇  肖建设 《冰川冻土》2009,31(6):1161-1165
利用青藏高原铁路沿线1961-2006年7个气象台站和2003年9月-2004年9月7个野外观测点风资料, 结合地理信息系统分析了青藏高原腹地微地形对极值风速的影响. 通过地形因子的参数化处理, 建立了极值风速随海拔和地形参数变化的拟合模型以精确推算复杂地形的极值风速, 并利用临时观测点风资料对其进行了检验. 结果表明, 利用地理信息系统和地形参数化处理方法研究青藏高原微地形对极值风速的影响具有可操作性.  相似文献   

13.
A mathematical model has been developed to forecast or hindcast wind, waves, and longshore currents during the passage of a coastal storm. Storm intensity is a function of the barometric pressure gradient which is modeled by rotating an inverted normal curve around the center of an ellipse. The length and orientation of the major and minor axes of the ellipse control the size and shape of the storm. The path of the storm is determined by a sequence of storm positions for the hindcast mode, and by interpolated positions assuming constant speed and direction for the forecast mode. The site location, shoreline orientation, and nearshore bottom slope provide input data for the shore position. The geostrophic wind speed and direction at the shore site are computed from the latitude and barometric pressure gradient. The geostrophic wind is converted into surface wind speed and direction by applying corrections for frictional effects over land and sea. The surface wind speed and direction, effective fetch, and wind duration are used to compute wave period, breaker height, and breaker angle at the shore site. The longshore current velocity is computed as a function of wave period, breaker height and angle, and nearshore slope. The model was tested by comparing observed data for several coastal locations with predicted values for wind speed, wave period and height, and longshore current velocity. Forecasts were made for actual storms and for hypothetical circular and elliptical storms.  相似文献   

14.
Landslide displacement is widely obtained to discover landslide behaviors for purpose of event forecasting. This article aims to present a comparative study on landslide nonlinear displacement analysis and prediction using computational intelligence techniques. Three state-of-art techniques, the support vector machine (SVM), the relevance vector machine (RVM), and the Gaussian process (GP), are comparatively presented briefly for modeling landslide displacement series. The three techniques are discussed comparatively for both fitting and predicting the landslide displacement series. Two landslides, the Baishuihe colluvial landslide in China Three Georges and the Super-Sauze mudslide in the French Alps, are illustrated. The results prove that the computational intelligence approaches are feasible and capable of fitting and predicting landslide nonlinear displacement. The Gaussian process, on the whole, performs better than the support vector machine, relevance vector machine, and simple artificial neural network (ANN) with optimized parameter values in predictive analysis of the landslide displacement.  相似文献   

15.
Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.  相似文献   

16.
Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.  相似文献   

17.
River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.  相似文献   

18.
An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor??s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09?%. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.  相似文献   

19.
This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.  相似文献   

20.
A procedure for cyclonic microzonation of coastal regions with the help of the cyclone track records is outlined using a sound method of statistical forecast and finding wind speed at a site with the help of standard wind field model. The procedure can be adopted for regions where directly measured wind speeds are scarce like, coastal regions of the developing and under developed countries. For the purpose of microzonation, the regions along with the available cyclone tracks are mapped using GIS. The area is then divided into a number of grids. The centre of the grid (site) is taken as the centre of the circle of influence. The cyclonic wind speeds at the site are estimated from the tracks falling within the influence circle. Distribution of the cyclonic wind speed at the site is then obtained from the estimated cyclonic wind speeds. Assuming the arrival of cyclone to be a Poisson process, a cyclone hazard curve, denoting the annual probability of exceedance versus cyclonic wind speed is determined. From the hazard curves drawn for different sites of the region, cyclonic microzonation map is prepared for different return periods of the cyclonic wind speed. The procedure is illustrated by applying it to microzone a very crucial coastal region of Andhra Pradesh in India, for which cyclone track records are available.  相似文献   

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