scholarly journals Advancing Remote Sensing and Machine Learning-Driven Frameworks for Groundwater Withdrawal Estimation in Arizona: Linking Land Subsidence to Groundwater Withdrawals

2021 ◽  
Author(s):  
Sayantan Majumdar ◽  
Ryan Smith ◽  
Brian Conway ◽  
V Lakshmi
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Subsidence ◽  
Management Practices ◽  
Absolute Error ◽  
The United States ◽  
Groundwater Withdrawal ◽  
Active Management ◽  
Global Food Security ◽  
South Central

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002-2020 at a 2 km spatial resolution using in-situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002-2009 for training and 2010-2020 for validating the model respectively. The results show high training (R≈ 0.86) and good testing (R≈ 0.69) scores with normalized mean absolute error (NMAE) ≈ 0.64 and normalized root mean square error (NRMSE) ≈ 2.36 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favorable comparison with independent water use proxies such as InSAR demonstrate the effectiveness and extensibility of our combined remote sensing and machine learning-based approach.

scholarly journals Integrating Remote Sensing and Machine Learning for Groundwater Withdrawal Estimation in Arizona

2020 ◽  
Author(s):  
Sayantan Majumdar ◽  
Ryan Smith ◽  
Brian Conway ◽  
James Butler ◽  
Venkataraman Lakshmi
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Groundwater Withdrawal

scholarly journals Integrating Remote Sensing and Machine Learning for Groundwater Withdrawal Estimation in Arizona

2020 ◽  
Author(s):  
Sayantan Majumdar ◽  
Ryan Smith ◽  
Brian Conway ◽  
James Butler ◽  
Venkataraman Lakshmi
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Groundwater Withdrawal

scholarly journals Predicting wildfire burned area in South Central US using integrated machine learning techniques

2019 ◽  
Author(s):  
Sing-Chun Wang ◽  
Yuxuan Wang
Keyword(s):  
United States ◽  
Machine Learning ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Environmental Drivers ◽  
Fire Season ◽  
Burned Area ◽  
Environmental Controls ◽  
South Central

Abstract. Occurrences of devastating wildfires have been on the rise in the United States for the past decades. While the environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between fires and the environmental controls are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area during 2002–2015 over the South Central United States and identify the relative importance of the environmental drivers on the burned area for both the winter-spring and summer fire seasons of that region. The developed model is able to alleviate the issue of unevenly-distributed burned area data and achieve a cross-validation (CV) R2 value of 0.42 and 0.40 for the two fire seasons. For the total burned area over the study domain, the model can explain 50 % and 79 % of interannual total burned area for the winter-spring and summer fire season, respectively. The prediction model ranks relative humidity (RH) anomalies and preceding months’ drought severity as the top two most important predictors on the gridded burned area for both fire seasons. Sensitivity experiments with the model show that the effect of climate change represented by a group of climate-anomaly variables contributes the most to the burned area for both fire seasons. Antecedent fuel amount and conditions are found to outweigh weather effects for the burned area in the winter-spring fire season, while the current-month fire weather is more important for the summer fire season likely due to the controlling effect of weather on fuel moisture in this season. This developed model allows us to predict gridded burned area and to access specific fire management strategies for different fire mechanisms in the two seasons.

scholarly journals Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America

2020 ◽  
Author(s):  
Mario Jojoa ◽  
Begoña Garcia-Zapirain
Keyword(s):  
United States ◽  
Machine Learning ◽  
Open Data ◽  
Absolute Error ◽  
The United States ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Tools ◽  
The European Union ◽  
Testing Stage

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

scholarly journals Regional Analysis of Production Practices and Technology Use in the U.S. Nursery Industry

HortScience ◽  
2008 ◽  
Vol 43 (6) ◽  
pp. 1807-1812 ◽  
Author(s):  
Alan W. Hodges ◽  
Charles R. Hall ◽  
Bridget K. Behe ◽  
Jennifer H. Dennis
Keyword(s):  
Native American ◽  
Technology Use ◽  
Management Practices ◽  
Regional Analysis ◽  
The United States ◽  
Total Production ◽  
Research Committee ◽  
South Central ◽  
Production Practices ◽  
Total Sales

The National Nursery Survey has been conducted four times at 5-year intervals (1988, 1993, 1998, and 2003) by a multistate research committee on economics and marketing to help fill the void of publicly available information on management characteristics of the nursery industry. For the first time in 2003, the National Nursery Survey was conducted using a standard sampling methodology with 15,588 total firms representing 44 states. The objective of this study was to provide a regional analysis of nursery production practices, because production practices and technology use may differ across regions in response to varying economic and environmental conditions. From analysis of the 2485 returned surveys, firms in the northern and interior regions of the country with more seasonal activity made greater use of temporary labor. Containerized growing systems were the predominant system throughout the United States; however, firms in the Southeast, South Central, and Pacific coast regions used this system to a greater degree, whereas firms in other regions also commonly used bare root and balled and burlapped systems. Nurseries in the Southeast region, with a warmer climate, used Integrated Pest Management practices more prevalently. Most regions had a significant share of total production from native American plants, approaching or exceeding 20% of total sales, except the Pacific region. In some regions, forward-contracting accounted for a significantly higher share of total sales, perhaps indicating greater aversion to market risk. The Mountain region stood out for its high level of adoption of computer technologies for production, marketing, and management. Data on water use and irrigation technology did not indicate any clear pattern with respect to regional differences in relation to water scarcity.

scholarly journals Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 870
Author(s):  
Josefina Mosre ◽  
Francisco Suárez
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Meteorological Data ◽  
Learning Algorithms ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Actual Evapotranspiration ◽  
Regression Equations ◽  
Cold Regions

Actual evapotranspiration (ETa) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ETa exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ETa in arid cold regions, where there is a lack of available ETa data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations. In situ ETa and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ETa in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ETa is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R2 = 0.82), whereas the worst performance of these equations was obtained for monthly ETa estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered.

scholarly journals The application of satellite remote sensing for assessing productivity in relation to fisheries yields of the world's large marine ecosystems

2011 ◽  
Vol 68 (4) ◽  
pp. 667-676 ◽  
Author(s):  
Kenneth Sherman ◽  
John O'Reilly ◽  
Igor M. Belkin ◽  
Christopher Melrose ◽  
Kevin D. Friedland
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Management Practices ◽  
The United States ◽  
Marine Ecosystems ◽  
Global Networks ◽  
Large Marine Ecosystems ◽  
Goods And Services ◽  
Current Period ◽  
Biomass Yields

Abstract Sherman, K., O'Reilly, J., Belkin, I. M., Melrose, C., and Friedland, K. D. 2011. The application of satellite remote sensing for assessing productivity in relation to fisheries yields of the world's large marine ecosystems. – ICES Journal of Marine Science, 68: 667–676. In 1992, world leaders at the historical UN Conference on Environment and Development (UNCED) recognized that the exploitation of resources in coastal oceans was becoming increasingly unsustainable, resulting in an international effort to assess, recover, and manage goods and services of large marine ecosystems (LMEs). More than $3 billion in support to 110 economically developing nations have been dedicated to operationalizing a five-module approach supporting LME assessment and management practices. An important component of this effort focuses on the effects of climate change on fisheries biomass yields of LMEs, using satellite remote sensing and in situ sampling of key indicators of changing ecological conditions. Warming appears to be reducing primary productivity in the lower latitudes, where stratification of the water column has intensified. Fishery biomass yields in the Subpolar LMEs of the Northeast Atlantic are also increasing as zooplankton levels increase with warming. During the current period of climate warming, it is especially important for space agency programmes in Asia, Europe, and the United States to continue to provide satellite-borne radiometry data to the global networks of LME assessment scientists.

Sign in / Sign up

Export Citation Format

Share Document