排序方式： 共有6条查询结果，搜索用时 187 毫秒
1
1.
Andrew F. Casper Barnali DixonEric T. Steimle Mike L. HallRobyn N. Conmy 《Applied geography (Sevenoaks, England)》2012,32(2):455-464
While the spatial heterogeneity of many aquatic ecosystems is acknowledged, rivers are often mistakenly described as homogenous and well-mixed. The collection and visualization of attributes like water quality is key to our perception and management of these ecosystems. The assumption of homogeneity can lead to the conclusion that data collection from discrete, discontinuous points in space or time provide a comprehensive estimate of condition. To counter this perception, we combined high-density data collection with spatial interpolation techniques to created two-dimensional maps of water quality. Maps of four riverine transitions and habitats - wetland to urban, river to reservoir, river to estuary and a groundwater intrusion - were constructed from the continuous data. The examples provided show that the most basic water quality parameters - temperature, conductivity, salinity, turbidity, and chlorophyll florescence - are heterogeneous at spatial scales smaller than those captured by common point sampling statistical strategies. The 2-dimensional, interpolation-based maps of the Hillsborough River (Tampa, FL) show significant influences of a variety of geographic features including tributary confluences, submarine groundwater inflow, and riparian interfaces. We conclude that many sampling strategies do not account for the type of patchy heterogeneity observed. The integration of existing in-situ sensors, inexpensive autonomous sampling platforms, and geospatial mapping techniques provides high resolution visualization that can adds a more comprehensive geographic perspective needed for environmental monitoring and assessment programs. 相似文献
2.
Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs 总被引：1，自引：0，他引：1
This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
3.
A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N 总被引：2，自引：0，他引：2
Barnali Dixon 《Hydrogeology Journal》2009,17(6):1507-1520
Accurate and inexpensive identification of potentially contaminated wells is critical for water resources protection and management. The objectives of this study are to 1) assess the suitability of approximation tools such as neural networks (NN) and support vector machines (SVM) integrated in a geographic information system (GIS) for identifying contaminated wells and 2) use logistic regression and feature selection methods to identify significant variables for transporting contaminants in and through the soil profile to the groundwater. Fourteen GIS derived soil hydrogeologic and landuse parameters were used as initial inputs in this study. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) were used as an output target class. The use of the logistic regression and feature selection methods reduced the number of input variables to nine. Receiver operating characteristics (ROC) curves were used for evaluation of these approximation tools. Results showed superior performance with the NN as compared to SVM especially on training data while testing results were comparable. Feature selection did not improve accuracy; however, it helped increase the sensitivity or true positive rate (TPR). Thus, a higher TPR was obtainable with fewer variables. 相似文献
4.
With continued population growth and increasing use of fresh groundwater resources, protection of this valuable resource is critical. A cost effective means to assess risk of groundwater contamination potential will provide a useful tool to protect these resources. Integrating geospatial methods offers a means to quantify the risk of contaminant potential in cost effective and spatially explicit ways. This research was designed to compare the ability of intrinsic (DRASTIC) and specific (Attenuation Factor; AF) vulnerability models to indicate groundwater vulnerability areas by comparing model results to the presence of pesticides from groundwater sample datasets. A logistic regression was used to assess the relationship between the environmental variables and the presence or absence of pesticides within regions of varying vulnerability. According to the DRASTIC model, more than 20% of the study area is very highly vulnerable. Approximately 30% is very highly vulnerable according to the AF model. When groundwater concentrations of individual pesticides were compared to model predictions, the results were mixed. Model predictability improved when concentrations of the group of similar pesticides were compared to model results. Compared to the DRASTIC model, the AF model more accurately predicts the distribution of the number of contaminated wells within each vulnerability class. 相似文献
5.
Barnali Goswami Gupinath Bhandari 《Journal of the Indian Society of Remote Sensing》2013,41(2):291-299
The most significant part of prediction of precipitation is the detection and identification of convective (cumulonimbus) clouds, also the tracking of cloud movement is important for identification of location of precipitation. A very simple methodology for detecting convective clouds and then tracking its movement from a series of infrared (IR) images is proposed in this paper. IR image is segmented using k-means clustering algorithm, which has been implemented using Euclidean, Manhattan and Mahalanobis distances and the results have been compared. Cloud clusters have been identified from segmented image and subsequently the large clusters were extracted. Center of Mass (CoM) was calculated for each selected cloud cluster and its position after every 30 min was predicted and compared with the actual values. If the predicted position deviates, the proposed models automatically adjusts itself, and the next prediction becomes closer to original values of position. 相似文献
6.
Bordbar Mojgan Neshat Aminreza Javadi Saman Pradhan Biswajeet Dixon Barnali Paryani Sina 《Natural Hazards》2022,110(3):1799-1820
The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.
Graphic abstract
1