RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

Liu, Guangping; Wei, Jingmei; Muthu, BalaAnand; Samuel, R. Dinesh Jackson

doi:10.1080/22797254.2020.1771774

Cited by 7 publications

(2 citation statements)

References 20 publications

(19 reference statements)

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“…The use of turbidity to estimate loads of coliform bacteria is attractive due to the rapidity with which turbidity can be determined automatically [45] through in situ sensors [46] or by drone [47] or satellite sensing [48,49]. In the present study, manual measurements of turbidity were made by trained citizen scientists using a simple calibrated Secchi tube.…”

Section: Plos Onementioning

confidence: 99%

Community monitoring of coliform pollution in Lake Tanganyika

et al. 2022

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Conventional water quality monitoring has been done for decades in Lake Tanganyika, under different national and international programs. However, these projects utilized monitoring approaches, which were temporally limited, labour intensive and costly. This study examines the use of citizen science to monitor the dynamics of coliform concentrations in Lake Tanganyika as a complementary method to statutory and project-focused measurements. Persons in five coastal communities (Kibirizi, Ilagala, Karago, Ujiji and Gombe) were trained and monitored total coliforms, faecal coliforms and turbidity for one year on a monthly basis, in parallel with professional scientists. A standardized and calibrated Secchi tube was used at the same time to determine turbidity. Results indicate that total and faecal coliform concentrations determined by citizen scientists correlated well to those determined by professional scientists. Furthermore, citizen scientist-based turbidity values were shown to provide a potential indicator for high FC and TC concentrations. As a simple tiered approach to identify increased coliform loads, trained local citizen scientists could use low-cost turbidity measurements with follow up sampling and analysis for coliforms, to inform their communities and regulatory bodies of high risk conditions, as well as to validate local mitigation actions. By comparing the spatial and temporal dynamics of coliform concentrations to local conditions of infrastructure, population, precipitation and hydrology in the 15 sites (3 sites per community) over 12 months, potential drivers of coliform pollution in these communities were identified, largely related to precipitation dynamics and the land use.

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Section: Plos Onementioning

confidence: 99%

Community monitoring of coliform pollution in Lake Tanganyika

et al. 2022

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“…Many of these developments threaten long‐standing financial services companies' business strategies. (Liu et al, 2021) In recent years, the IoT has run into various problems related to FinTech and distributed ledger technologies. (Sutrala et al, 2021; Manogaran et al, 2021) The phrase distributed ledger refers to systems that retain data in a fragmented or dispersed way over a network of computers.…”

Section: Introduction Of Distributed Hyper Ledger Technology In Fintechmentioning

confidence: 99%

Distributed hyperledger technology in FinTech with artificial intelligence assisted internet of things platform

Zhou

et al. 2022

Expert Systems

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Artificial intelligence (AI) and distributed hyper ledger technology are both at the height of their hype cycles, which means they have the potential to be disruptive. This raises issues about new technologies' influence on future business structures, particularly in service‐based industries like finance. Even though numerous assumptions in reality point to the complementary use of these two technologies to produce new value creation potentials, there is little contemporary literature and study on the subject. Different perspectives on any of the two technologies mentioned above are insufficient for understanding potential synergies in financial services. Similarly, the internet of things (IoT) assistance for FinTech solutions has risen its significance in the upcoming transaction world. Therefore, this research proposes a distributed hyper ledger technology and AI for IoT‐based FinTech platform (DHLT‐AI‐FTP), which incorporates the benefits of each technology for secured and energy‐efficient financial transactions and communication. The AI technology and distributed hyper ledger technology handle the security part, and the communication part holds the efficient design of IoT implementation. The simulation evaluation of the proposed model ensures the improved security and efficiency of FinTech management and processing for real‐time business scenarios. The experimental analysis shows the significant performance of the proposed DHLT‐AI‐FTP with a better adaptability ratio of 98.56% compared to other existing approaches.

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Application of Modern GIS and Remote Sensing Technology Based on Big Data Analysis in Intelligent Agriculture

Wang

Wen

et al. 2022

J Indian Soc Remote Sens

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RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

Cited by 7 publications

References 20 publications

Community monitoring of coliform pollution in Lake Tanganyika

Community monitoring of coliform pollution in Lake Tanganyika

Distributed hyperledger technology in FinTech with artificial intelligence assisted internet of things platform

Application of Modern GIS and Remote Sensing Technology Based on Big Data Analysis in Intelligent Agriculture

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