scholarly journals Mapping Irrigated Areas Using MODIS 250 Meter Time-Series Data: A Study on Krishna River Basin (India)

Water ◽  
2011 ◽  
Vol 3 (1) ◽  
pp. 113-131 ◽  
Author(s):  
Murali Krishna Gumma ◽  
Prasad S. Thenkabail ◽  
Andrew Nelson
Keyword(s):  
Time Series ◽  
River Basin ◽  
Time Series Data ◽  
Series Data

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

2011 ◽  
Vol 32 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Kaishan Song ◽  
Zongmin Wang ◽  
Qingfeng Liu ◽  
Dianwei Liu ◽  
V. V. Ermoshin ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Amur River Basin ◽  
Land Use Land Cover ◽  
Amur River ◽  
The Amur River

scholarly journals Temporal Variation of Rainfall in the Bagmati River Basin, Nepal

2016 ◽  
Vol 16 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Rajendra Man Shrestha ◽  
Azaya Bikram Sthapit
Keyword(s):  
Time Series ◽  
Temporal Variation ◽  
River Basin ◽  
Summer Monsoon ◽  
Time Series Data ◽  
Monsoon Season ◽  
Series Data ◽  
Upward Trend ◽  
Areal Rainfall ◽  
Bagmati River

The main aim of the study was to identify the temporal variation of rainfall in the Bagmati River basin, Nepal using  data available at Department of Hydrology and Meteorology, Government of Nepal. The time series data for the  period of 1981-2008 were analyzed by using non-parametric Mann-Kendal test, Spearman’s’ Rho and a parametric  linear regression. The results showed that there was a significantly increasing upward trend of the annual mean of  weighted areal rainfall, with a rate of 2.2 mm per year. Trend analysis of the monthly time series of weighted areal  rainfall showed a significant upward trend in the months of summer monsoon season (June and July). However,  there were no such significant result in the other season/months. The increasing trend in the summer monsoon  might lead to severe flooding in future.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 31-40

scholarly journals FOREST DISTURBANCE DETECTION BY LANDSAT-BASED NDVI TIME SERIES FOR AYUQUILA RIVER BASIN, JALISCO, MEXICO

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 31-35
Author(s):  
Y. Gao ◽  
A. Quevedo ◽  
J. Loya
Keyword(s):  
Time Series ◽  
River Basin ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Series Data ◽  
Forest Loss ◽  
Validation Data ◽  
Early Action ◽  
Ndvi Time Series ◽  
Disturbance Detection

Abstract. Time series data have been applied for forest disturbance detection. The validation of detected changes is challenging partially because the validation data are often not readily available. Unlike multi-temporal change analysis, time series analysis not only detects areas of change but also reports time of change. Both spatial and temporal accuracy are therefore important for the accuracy assessment. Ayuquila River Basin (ARB) is one of the early action areas in Mexico for the implementation of REDD+ initiatives under UNFCCC. In ARB, shifting cultivation and cattle grazing often take place, resulting in degraded forestland. Sub-annual forest disturbance detection and estimation contribution to the improved local forest management and REDD+ implementation. Landsat-based NDVI time series data covering 1999–2018 were analysed using linear regression and the breakpoints of change and the magnitude of change were detected. Breakpoints with magnitude of change ranging from (−0.05) to (−0.2) were verified during a field campaign in October 2018. Here the magnitude of change is related with NDVI differences. Areas with magnitude of change higher than (−0.2) were identified as false changes. Verification data were generated by visually interpreting time series Landsat images of 2016–2018. In this way, areas with forest loss were identified. By stratified random sampling, 683 points were applied for the verification including 511 points of forests and 172 points of forest loss. It yields 75.84% for the overall accuracy of the change detection; for the detected forest loss as a category, the user’s accuracy is 88.89% and the producer’s accuracy is 0.46%. A possible reason for the very low producer’s accuracy is that the selected magnitude value (−0.2) is too low and some of the detected changes were filtered out.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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