scholarly journals Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4010
Author(s):  
Xiang Liu ◽  
Huiyu Liu ◽  
Pawanjeet Datta ◽  
Julian Frey ◽  
Barbara Koch
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spartina Alterniflora ◽  
Vegetation Index ◽  
Invasive Plant ◽  
Training Data ◽  
Support Vector ◽  
Detection Scheme ◽  
Series Analysis ◽  
Ndvi Time Series

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

scholarly journals Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications

2019 ◽  
Vol 11 (11) ◽  
pp. 1370 ◽  
Author(s):  
Petar Dimitrov ◽  
Qinghan Dong ◽  
Herman Eerens ◽  
Alexander Gikov ◽  
Lachezar Filchev ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Training Data ◽  
Support Vector ◽  
Pixel Classification ◽  
Good Correspondence ◽  
Winter Cereals ◽  
Ndvi Time Series ◽  
Sentinel 2

This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 × 10 km2, especially when the SVR method was used. For the five dominant classes in the test sites the R2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used.

scholarly journals An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Zeynep Hilal Kilimci ◽  
A. Okay Akyuz ◽  
Mitat Uysal ◽  
Selim Akyokus ◽  
M. Ozan Uysal ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Support Vector Regression ◽  
Historical Data ◽  
Demand Forecasting ◽  
Support Vector ◽  
Series Analysis ◽  
Integration Strategy ◽  
Forecasting System

Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals Forecasting variance of NiftyIT index with RNN and DNN

2022 ◽  
Vol 2161 (1) ◽  
pp. 012005
Author(s):  
C R Karthik ◽  
Raghunandan ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Network Models ◽  
Training Data ◽  
Complex Data ◽  
Neural Network Models ◽  
Series Analysis

Abstract A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

scholarly journals Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

2017 ◽  
Author(s):  
Tao Wen ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Chengyuan Lin ◽  
Chengren Xiong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Series Analysis ◽  
Trend Component ◽  
Geological Conditions ◽  
Component Displacement

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall, and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with step-like deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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