Patakamuri, Sandeep Kumar, Krishnaveni Muthiah, and Venkataramana Sridhar. "Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India." Water, 12, no. 1 (2020): 211.
Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to time series unaffected by serial correlation. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free CUSUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement all the statistical tests mentioned in this paper.
Patakamuri, Sandeep Kumar, and Nicole O'Brien. "Modifiedmk: Modified Mann Kendall Trend Tests."R package version 1.5.0, (2020).
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
Patakamuri, Sandeep Kumar, and Bappa Das. "trendchange: Innovative Trend Analysis and Time-Series Change Point Analysis." R package version 1.1.0, (2019).
Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
Krishnaveni Muthiah, Sandeep Kumar Patakamuri and Rajeswari Athikesavan. “Route and optimal location analysis of egg supplychain using geo-spatial technology.” Journal of Geomatics, 11, no. 1 (2017): 1–6.
Supply chain of eggs is marred with inefficiencies starting from field level, till it reaches the customers. Present work concentrates on bringing down the cost of delivery of eggs from collection centres to the distribution centres by incorporating geospatial technologies in identifying optimal route rather than following vehicle driver’s own discretion. Data pertaining to eggs handled per day, travel routes, travel time and fuel expenses etc., are collected by conducting on-field questionnaire survey. GPS survey was conducted to collect the spatial information of collection centres and distribution centres. The study results in identifying three optimal routes and also to identify four suitable sites for establishing new distribution centres. The optimal route identified in the study reduces the travelling distance by approximately 40km per day and thereby reducing fuel consumption.
Krishnaveni Muthiah, Sandeep Kumar Patakamuri and Rajeswari Athikesavan. “Solar Energy Potential Estimation and Its Utility for Irrigation Using Geo-Spatial Technology to Ensure Energy Security.” Water and Energy International 59(2), no. May (2016): 34–38.
World development is highly dependent on sustainable utilization of energy resources. Conventional energy generation through fossil fuels is no longer a viable solution to meet ever growing demand. Renewable energy is the key to solve the problems of energy sector due to its potential. In present study, energy demand of the farmers for irrigation purpose is identified through participatory approaches and the data pertaining to existing supply of electricity is obtained from governmental agencies. Solar insolation varies over space depending on the topographic conditions and atmospheric conditions and also based on the day and season of the year. Using the in-situ solar irradiance data, elevation data and land use data, the solar energy potential is estimated for the district of Namakkal, Tamilnadu using geo-spatial technology. The annual average solar potential of the study area is found to be 5.2 kWh/sq.m/day. The study also estimated the cost incurred by an individual in installing 5kW systems and the time period of return on investment. It is found that the study area has enormous potential to tap solar energy and can solve the power crisis in irrigation sector.
Muruganantham, M.; Krishnaveni, M.; Patakamuri, Sandeep Kumar. “Performance Evaluation of Uthiramerur Tank Irrigation System Using Spatial Technologies and Participatory Approach.” Journal of Applied Hydrology XXVIII, no. 1&2 (2015): 79–86.
Evaluating the performance of irrigation systems plays a vital role in attaining agricultural sustainability. Performance measures incorporated in an irrigation system monitoring program can provide a framework for assessing system improvement alternatives. Conventional methods of monitoring and evaluating irrigation system are tedious and involves huge economic and manual resources. Baseline inventory of irrigated lands in spatial and time domains using spatial information technologies provide an array of performance evaluation. Performance evaluation is a major component of proper management, which in turn is the basis for optimal use of land and water resources. In the present study, questionnaire survey was carried out alongside remote sensing and GIS based analysis to identify problems in adequate, reliable and equitable supply of water delivery system and to analyze the water delivery performance of Uthiramerur tank. The study showed an alarming changes in the tank performance, land use and farm productivity and lack of agreement among the stakeholders of water user associations. If the situation prevails in similar manner, would pose a great threat to agricultural sustainability and nation’s food security.
Patakamuri, S K, S Agrawal, and M Krishnaveni. “Time-Series Analysis of MODIS NDVI Data along with Ancillary Data for Land Use/Land Cover Mapping of Uttarakhand.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40, no. December (2014): 1491–1500.
Land use and land cover plays an important role in biogeochemical cycles, global climate and seasonal changes. Mapping land use and land cover at various spatial and temporal scales is thus required. Reliable and up to date land use/land cover data is of prime importance for Uttarakhand, which houses twelve national parks and wildlife sanctuaries and also has a vast potential in tourism sector. The research is aimed at mapping the land use/land cover for Uttarakhand state of India using Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The study also incorporated smoothening of time-series plots using filtering techniques, which helped in identifying phenological characteristics of various land cover types. Multi temporal Normalized Difference Vegetation Index (NDVI) data for the year 2010 was used for mapping the Land use/land cover at 250m coarse resolution. A total of 23 images covering a single year were layer stacked and 150 clusters were generated using unsupervised classification (ISODATA) on the yearly composite. To identify different types of land cover classes, the temporal pattern (or) phenological information observed from the MODIS (MOD13Q1) NDVI, elevation data from Shuttle Radar Topography Mission (SRTM), MODIS water mask (MOD44W), Nighttime Lights Time Series data from Defense Meteorological Satellite Program
(DMSP) and Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data were used. Final map product is generated by adopting hybrid classification approach, which resulted in detailed and accurate land use and land cover map.