A new big data approach based on geoecological information-modeling system

Varotsos, C.; Krapivin, Vladimir F.

doi:10.1080/20964471.2017.1397405

Cited by 39 publications

(13 citation statements)

References 58 publications

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“…Finally, simulation results are given in Tables 3 and 4, which show hydrological elements of the Aral Sea Basin in their dynamics after the EPS-4 realization. These results demonstrate the existence of an effective strategy for the management of the Central Asia water resources using the EPM manipulations and tools for the big data processing [56].…”

Section: Monthmentioning

confidence: 70%

Hydrological Model for Sustainable Development in the Aral Sea Region

2019

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Possible scenarios of the Aral Sea crisis solution are discussed, and a new scenario is proposed. Previous scenarios have provided for the transfer of water from Siberian Rivers to Central Asia and the restriction of unsustainable expansion of irrigation in this region. The scenario proposed in this paper is partly based on the use of Caspian water evaporators located on the eastern coast of the Caspian Sea. Engineering realization of this scenario needs only the construction of the drainage system for the runoff of Caspian waters to the natural evaporators, between which Kara-Bogaz-Gol is the functioning evaporator. This paper shows that realization of this scenario allows the rescue of the Aral Sea and normalization of the water balance in Central Asia. Under this, as the simulation modeling results show, there exist different versions of the scenario depending on the area of evaporators and restrictions for the runoff of Amu Darya and Syr Darya waters to the irrigation systems. Calculation results show that the Aral Sea could be restored within 90–240 years depending on the scenario versions. With only Kara-Bogaz-Gol as the evaporator, the Aral Sea cannot be restored within a century. Additionally, if the anthropogenic runoff of river waters was decreased by 10 percent, the Aral Sea would be restored over about 90 years. Possible versions of the recovery scenario are discussed and assessed.

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Section: Monthmentioning

confidence: 70%

Hydrological Model for Sustainable Development in the Aral Sea Region

2019

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“…Its technical nature is analyzed. This has guiding significance for the research and engineering application of big data [5].…”

Section: Literature Reviewmentioning

confidence: 94%

Water Conservancy Data Acquisition and Big Data Service Based on Multi-data Sources

Zhu¹

2019

JEIS

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To reflect the application value of data development, based on the data of multiple data sources, the water conservancy and the big data service were studied. First, the acquisition of public data was studied. Computers were used to quickly and efficiently collect data into libraries, which greatly reduce the difficulty of data acquisition. Then, the method of data cleaning was determined to improve data quality and enhance the effectiveness and reliability of the data in the application process. Finally, the water conservancy prediction model was applied to the flood prevention decision-making service system based on the integrated platform. The results showed that the acquisition of public data greatly improved the efficiency of data acquisition. By cleaning the obtained data of repeated values, error values, outliers and missing values, higher quality water situation data was obtained. The water conservancy prediction model improved the accuracy of the prediction, and the flood control decision service system provided an efficient and operational integrated platform. Therefore, the water conservancy prediction model has a certain guiding role in flood control decision-making. It is the key to big data services for water conservancy.

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“…Varotsos and Krapivin [15] have developed an instrumental information-modeling method that enables decision making when available data are characterized as episodic and fragmented in time and space, respectively. This paper which develops this method proposes a new approach to improve the monitoring of Siberian forests.…”

Section: Remote Sensing Platformmentioning

confidence: 99%

“…This model is better than many similar models. In practice, all existing forest fire risk indices are based on the available data provided by national or global monitoring systems whose capabilities are identified using big data processing tools [15].…”

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confidence: 99%

A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

2020

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The purpose of this paper is to present a new method for early detection of forest fires, especially in forest zones prone to fires using microwave remote sensing and information-modeling tools. A decision-making system is developed as a tool for operational coupled analysis of modeling results and remote sensing data. The main operating structure of this system has blocks that calculate the moisture of forest canopy, the soil-litter layer, and the forest physical temperature using the observed brightness temperature provided by the flying platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies. The hydrological parameters of the forest are assessed with both a developed regional hydrological model and remote sensing observations. The hydrological model allows for the detection of fire-prone zones that are subject to remote sensing when modeling results are corrected and thermal temperatures are evaluated. An approach for the real time forest fires classification via daytime remote sensing observations is proposed. The relative theoretical and experimental results presented here have allowed us to use a new approach to forests monitoring during periods of potential fire. A decision-making algorithm is presented that aims at analyzing data flows from radiometers located on the remote sensing platform to calculate the probability of forest fire occurring in geographical pixels. As case study, the state of forest fires that occurred in Siberia in 2019 using microwave remote sensing measurements conducted by a flying IL-18 laboratory is presented. This remote sensing platform is equipped with optical and microwave tools that allow the optical and microwave images of the observed forest areas. The main operating frequencies of microwave radiometers are 1.43, 13.3 and 37.5 GHz. Microwave radiometers provide data on water content in the forest canopy and on litter and physical temperatures. Based on the long-term measurements made in Siberia, the possible improvement of the proposed decision-making system for future relevant studies is discussed in detail. The basic idea of cost-effective monitoring of forested areas consists of a two-stage exploration of fire risk zones. The first monitoring stage is performed using the hydrological model of the study area to identify low moisture areas of the forest canopy and litter. The second stage of monitoring is conducted using the remote sensing platform only in the local fire-dangerous areas in order to more precisely identify the areas prone to fire and to detect and diagnose real burning zones. The developed algorithm allows the calculation of physical temperatures and the detection of temperature anomalies based on measured brightness temperatures. Finally, the spatial distribution of the probability of forest fire occurrence is given as an example of the decision-making system along with a comparison of this distribution with the satellite images provided by the EOSDIS Land data.

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A new big data approach based on geoecological information-modeling system

Cited by 39 publications

References 58 publications

Hydrological Model for Sustainable Development in the Aral Sea Region

Hydrological Model for Sustainable Development in the Aral Sea Region

Water Conservancy Data Acquisition and Big Data Service Based on Multi-data Sources

A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

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