scholarly journals A Study of Avian Population Recovery after Habitat Restoration Using Remote Sensing and Community Science Bird Observation Data

2019 ◽  
Keyword(s):  
Habitat Restoration ◽  
Supervised Machine Learning ◽  
Climate Indices ◽  
Landsat 8 ◽  
Observation Data ◽  
Climate Data ◽  
Machine Learning Classification ◽  
Community Science ◽  
Wetland Habitats ◽  
Bird Observation

During the last two centuries, the contiguous United States has lost over half of its wetland habitats. Preserving the remaining wetland habitats and reversing this trend is of critical importance. Fernhill Wetlands in Forest Grove, Oregon is a natural wastewater treatment site that was transformed from unused wastewater ponds to a complex natural wetland habitat in 2014-15. To assess restoration effect on biodiversity changes, previous studies involved manual observations of changes to bird populations. In this study, LANDSAT-8 and SENTINEL-2 satellite imagery and PRISM climate data were used to calculate vegetation, water and climate indices for Fernhill Wetlands for pre- and post-restoration periods, ranging from 2013 to 2018. Then, for the first time quantitative correlations were established between these indices and community science bird observation data from the Cornell University eBird database. The study showed previously unobserved effects of the habitat restoration, positive and negative, on several species. Shorebirds, marsh birds and others that lived at the water’s edge showed much subtler and sometimes unexpected reactions to the habitat change. Further, supervised machine learning classification was used to obtain clarity on land, vegetation and water changes in the region of interest. This study could be of interest to wetland managers to help guide further habitat modifications.

The effects of climate on corn price movements in the United States

2021 ◽  
Author(s):  
Qiaoyu Deng ◽  
Xun Sun
Keyword(s):  
Commodity Price ◽  
The United States ◽  
High Temperature Stress ◽  
Multiple Regression Model ◽  
Climate Indices ◽  
Observation Data ◽  
Climate Data ◽  
Price Variation ◽  
Growing Period ◽  
Price Movements

<p>Corn is the 1st economic field crop in the world, whose price stability guarantees sustainable and equitable food security. Most previous farm commodity price prediction model only focus on detecting the autoregression of historical transaction, while ignoring other factors. For agricultural commodities, different climate condition leads to different harvest situation, thus bringing volatility to prices. Therefore, it is reasonable to propose a method based on climate indices to measure the degree of their influence on price fluctuation.</p><p>A multiple regression model is developed for predicting corn price movements at the nation level. The June-September season is selected to target the essential growing stages of corn which are especially sensitive to drought, high temperature stress and water stress. In order to describe the movements of price, the price difference between June and September is chosen as the dependent variable. Daily climate data are obtained from PRISM which integrates both satellite and meteorological station observation data, and monthly price data are sourced from USDA. 39-year trend from 1981-2019 is explored to construct a predictive model. The results show that the accuracy of predicting up and down of price is 85%. Specifically, temperature in July has an identifiable effect on price movements which explains 36.99% price variation. These results imply that during the key growing period, climate indices occupy an important position on improving crop price forecast ability.</p>

scholarly journals North Pacific warming shifts the juvenile range of a marine apex predator

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kisei R. Tanaka ◽  
Kyle S. Van Houtan ◽  
Eric Mailander ◽  
Beatriz S. Dias ◽  
Carol Galginaitis ◽  
...  
Keyword(s):  
North Pacific ◽  
Species Conservation ◽  
Total Body Length ◽  
Suitable Habitat ◽  
Climate Data ◽  
Marine Predator ◽  
Spatial Shift ◽  
Marine Megafauna ◽  
Community Science ◽  
Juvenile Sharks

AbstractDuring the 2014–2016 North Pacific marine heatwave, unprecedented sightings of juvenile white sharks (Carcharodon carcharias) emerged in central California. These records contradicted the species established life history, where juveniles remain in warmer waters in the southern California Current. This spatial shift is significant as it creates potential conflicts with commercial fisheries, protected species conservation, and public safety concerns. Here, we integrate community science, photogrammetry, biologging, and mesoscale climate data to describe and explain this phenomenon. We find a dramatic increase in white sharks from 2014 to 2019 in Monterey Bay that was overwhelmingly comprised of juvenile sharks < 2.5 m in total body length. Next, we derived thermal preferences from 22 million tag measurements of 14 juvenile sharks and use this to map the cold limit of their range. Consistent with historical records, the position of this cold edge averaged 34° N from 1982 to 2013 but jumped to 38.5° during the 2014–2016 marine heat wave. In addition to a poleward shift, thermally suitable habitat for juvenile sharks declined 223.2 km2 year−1 from 1982 to 2019 and was lowest in 2015 at the peak of the heatwave. In addition to advancing the adaptive management of this apex marine predator, we discuss this opportunity to engage public on climate change through marine megafauna.

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals The European 2015 drought from a hydrological perspective

2017 ◽  
Vol 21 (6) ◽  
pp. 3001-3024 ◽  
Author(s):  
Gregor Laaha ◽  
Tobias Gauster ◽  
Lena M. Tallaksen ◽  
Jean-Philippe Vidal ◽  
Kerstin Stahl ◽  
...  
Keyword(s):  
Water Cycle ◽  
Hydrological Drought ◽  
Low Flow ◽  
Drought Indices ◽  
Climate Indices ◽  
Climate Data ◽  
Spatial And Temporal Scales ◽  
Space And Time ◽  
Dynamic Development ◽  
The Difference

Abstract. In 2015 large parts of Europe were affected by drought. In this paper, we analyze the hydrological footprint (dynamic development over space and time) of the drought of 2015 in terms of both severity (magnitude) and spatial extent and compare it to the extreme drought of 2003. Analyses are based on a range of low flow and hydrological drought indices derived for about 800 streamflow records across Europe, collected in a community effort based on a common protocol. We compare the hydrological footprints of both events with the meteorological footprints, in order to learn from similarities and differences of both perspectives and to draw conclusions for drought management. The region affected by hydrological drought in 2015 differed somewhat from the drought of 2003, with its center located more towards eastern Europe. In terms of low flow magnitude, a region surrounding the Czech Republic was the most affected, with summer low flows that exhibited return intervals of 100 years and more. In terms of deficit volumes, the geographical center of the event was in southern Germany, where the drought lasted a particularly long time. A detailed spatial and temporal assessment of the 2015 event showed that the particular behavior in these regions was partly a result of diverging wetness preconditions in the studied catchments. Extreme droughts emerged where preconditions were particularly dry. In regions with wet preconditions, low flow events developed later and tended to be less severe. For both the 2003 and 2015 events, the onset of the hydrological drought was well correlated with the lowest flow recorded during the event (low flow magnitude), pointing towards a potential for early warning of the severity of streamflow drought. Time series of monthly drought indices (both streamflow- and climate-based indices) showed that meteorological and hydrological events developed differently in space and time, both in terms of extent and severity (magnitude). These results emphasize that drought is a hazard which leaves different footprints on the various components of the water cycle at different spatial and temporal scales. The difference in the dynamic development of meteorological and hydrological drought also implies that impacts on various water-use sectors and river ecology cannot be informed by climate indices alone. Thus, an assessment of drought impacts on water resources requires hydrological data in addition to drought indices based solely on climate data. The transboundary scale of the event also suggests that additional efforts need to be undertaken to make timely pan-European hydrological assessments more operational in the future.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

scholarly journals Inclusive Design of Earth Observation Decision Support Systems for Environmental Governance: A Case Study of Lake Nokoué

2021 ◽  
Vol 3 ◽  
Author(s):  
Ufuoma Ovienmhada ◽  
Fohla Mouftaou ◽  
Danielle Wood
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Earth Observation ◽  
Inclusive Design ◽  
Observation Data ◽  
Climate Data ◽  
Technology Design ◽  
Data Services

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.

scholarly journals The Central Arizona Conservation Alliance Programs: Use of Social Media and App-Supported Community Science for Landscape-Scale Habitat Restoration, Governance Support, and Community Resilience-Building

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 137
Author(s):  
Aireona B. Raschke ◽  
Jeny Davis ◽  
Annia Quiroz
Keyword(s):  
Social Media ◽  
Habitat Restoration ◽  
Positive Impact ◽  
Community Resilience ◽  
Open Spaces ◽  
Awareness Raising ◽  
Use Of Social Media ◽  
Community Science ◽  
Central Arizona

Land managers are currently faced with a nexus of challenges, both ecological and social, when trying to govern natural open spaces. While social media has led to many challenges for effective land management and governance, the technology has the potential to support key activities related to habitat restoration, awareness-raising for policy changes, and increased community resilience as the impacts of increased use and climate change become more apparent. Through the use of a case study examining the work of the Central Arizona Conservation Alliance’s social media ambassadorship and its app-supported community science projects, we examine the potential and realized positive impact that technology such as social media and smartphone apps can create for land managers and surrounding communities.

Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features

2021 ◽  
pp. 177-191
Author(s):  
Natalia V. Revollo ◽  
G. Noelia Revollo Sarmiento ◽  
Claudio Delrieux ◽  
Marcela Herrera ◽  
Rolando González-José
Keyword(s):  
Machine Learning ◽  
Human Sperm ◽  
Sperm Head ◽  
Morphological Features ◽  
Supervised Machine Learning ◽  
Machine Learning Classification

scholarly journals Evaluating the Tourist Climate Comfortable Period of China in a Changing Climate

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dan-Dan Yu ◽  
Shan Li ◽  
Zhong-Yang Guo
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Scientific Evidence ◽  
Climatic Conditions ◽  
Climate Index ◽  
Climate Indices ◽  
Climate Data ◽  
Climate Conditions ◽  
Positive Effects ◽  
Changing Climate

The evaluation of climate comfort for tourism can provide information for tourists selecting destinations and tourism operators. Understanding how climate conditions for tourism evolve is increasingly important for strategic tourism planning, particularly in rapidly developing tourism markets like China in a changing climate. Multidimensional climate indices are needed to evaluate climate for tourism, and previous studies in China have used the much criticized “climate index” with low resolution climate data. This study uses the Holiday Climate Index (HCI) and daily data from 775 weather stations to examine interregional differences in the tourist climate comfortable period (TCCP) across China and summarizes the spatiotemporal evolution of TCCP from 1981 to 2010 in a changing climate. Overall, most areas in China have an “excellent” climate for tourism, such that tourists may visit anytime with many choices available. The TCCP in most regions shows an increasing trend, and China benefits more from positive effects of climate change in climatic conditions for tourism, especially in spring and autumn. These results can provide some scientific evidence for understanding human settlement environmental constructions and further contribute in improving local or regional resilience responding to global climate change.

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