Spatial cumulative sum algorithm with big data analytics for climate change detection

2018 ◽  
Vol 65 ◽  
pp. 207-221 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Change Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cumulative Sum ◽  
Climate Change Detection

scholarly journals Big Data and Climate Change

2019 ◽  
Vol 3 (1) ◽  
pp. 12 ◽  
Author(s):  
Hossein Hassani ◽  
Xu Huang ◽  
Emmanuel Silva
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Climate Science ◽  
Current Status ◽  
Future Research ◽  
Climate Data ◽  
Data Intensive ◽  
Big Data Applications

Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

scholarly journals Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 3521-3526
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Seed Quality ◽  
Big Data Analytics ◽  
Computational Techniques ◽  
Yield Prediction ◽  
Crops Yield

Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture. There are several challenges of crops yield prediction in the field of precision agriculture are (i). Obtain minimized production due to climate change; (ii). Lead to different diseases; (iii). Availability of Water; (iv). No awareness of fertilizers and crop features; (v). Climate change; (vi). Unexpected weather events.Other loss factors in the agriculture are lowly seed quality, unplanned irrigation and exploitation of insecticides and fertilizers. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model. Hence in this research our focus is on optimizing the significant parameters such as rainfall, temperature and fertilizers rate to obtain the P-values for testing the crop and also analyze the human health safety (farmers and suppliers) due to the dynamic change of environment and also soil nutrients. Big data analytics is the feasible platform to test and measure the crop grow in the particular agriculture field. It helps in climate, weather events prediction and also it is used to compute the sufficient resources for crop cultivation.

scholarly journals N-HyDAA - Big Data Analytics for Malaysia Climate Change Knowledge Management

10.29007/5b3v ◽  
2018 ◽  
Author(s):  
Mohammad Fikry Abdullah ◽  
Mohd Zaki Mat Amin ◽  
Mohd Fauzi Mohamad ◽  
Marini Mohamad Ideris ◽  
Zurina Zainol ◽  
...  
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Economic Losses ◽  
Weather Extremes ◽  
Related Data ◽  
Water Stress Condition ◽  
Business Entities ◽  
Knowledge Portal

With the changing climate, the prognosis is that weather extremes such as floods, drought and EL Nino are likely to increase in frequency and intensity can expand billions of economic losses and effect human lives. NAHRIM Hydroclimate Data Analysis Accelerator (N-HyDAA), known as Malaysia Climate Change (CC) Knowledge Portal, the only CC knowledge portal in Malaysia primarily developed for providing CC and water-related data, information, knowledge and technologywhich is crucial for present and future water related bussines activities, engineering practices and environment. It has eight hydroclimate-environment modules, which amongst others are rainfall, floods, droughts and water stress condition using Big Data Analytics (BDA) technology by means of comprehensive analysis and interactive visualization tools. N-HyDAA is able to trace, detect, identify and visualise future water issues associated with the adverse impacts of climate change in Malaysia. N-HyDAA assist business entities, water operators, engineers, planners and decision-makers in designing, planning and developing water related program and risk management in combating climate change impact either mitigation or adaptation actions.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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