Ecological index prediction method based on the mangrove forest ecological big data

A technology of ecological indicators and prediction methods, applied in data processing applications, complex mathematical operations, instruments, etc., can solve the problems of sensing and image technology that are difficult to evaluate and predict the ecological health of mangroves

Active Publication Date: 2018-09-11
CHONGQING UNIV
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Problems solved by technology

However, sensing and image technology can only collect some ecological indicators of mangroves, such as water environment, sediment environmen

Method used

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  • Ecological index prediction method based on the mangrove forest ecological big data
  • Ecological index prediction method based on the mangrove forest ecological big data
  • Ecological index prediction method based on the mangrove forest ecological big data

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Embodiment 1

[0078] see Figure 1 to Figure 5 , an ecological index prediction method based on mangrove ecological big data, mainly includes the following steps:

[0079] 1) Determine the mangrove ecological protection area. In this embodiment, Beilun Estuary Ecological Protection Area is selected.

[0080] 2) Establish a dataset of mangrove ecological indicators.

[0081] The mangrove ecological index data information I collected by the mangrove ecological protection area in the previous 7 years using sensing and image technology is used as the data set T. The mangrove ecological index data information I mainly includes water quality information, sediment pH, soil pH and soil particle size indicators.

[0082] The water quality information mainly includes water temperature, salinity, pH, chlorophyll, ammonia nitrogen, nitrate, nitrite, inorganic phosphorus, petroleum and chemical oxygen demand.

[0083] Some indicators that can most directly reflect the ecological health of mangroves,...

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Abstract

The invention provides an ecological index prediction method based on the mangrove forest ecological big data, which mainly comprises the following steps of 1) determining the ecological protection area of the mangrove forest; (2) establishing a mangrove forest ecological index data set; 3) preprocessing the data set T and the data set Y; 4) performing linearization processing on the data set T toobtain a linear training set S; 5) establishing a mangrove forest ecological index prediction model; 6) by utilizing the mangrove forest ecological index prediction model, the linear training set S and the mangrove forest ecological health evaluation grade judgment table, predicting the future ecological health condition of the mangrove forest in the mangrove forest ecological protection area. According to the method, the predicted important ecological indexes are utilized, and a mangrove forest ecological health evaluation grade judgment table is combined, so that the ecological health condition of the mangrove forest can be accurately predicted.

Description

technical field [0001] The invention relates to a data mining technology and a deep learning method, in particular to an ecological index prediction method based on mangrove ecological big data. Background technique [0002] Mangroves are woody plant communities that grow in the intertidal zone of tropical and subtropical coasts and are periodically submerged by seawater. It plays an irreplaceable function in purifying pollution and protecting wetland diversity. The coast of Guangxi is an important mangrove distribution area in mainland China. The mainland coastline is 1490km long, and the mangrove area is the second largest in the country. It is the province with the largest mangrove distribution area per unit coastline length. There are 3 mangrove nature reserves in Guangxi (Shankou National Nature Reserve in Hepu, Guangxi, Beilun River Estuary National Nature Reserve in Fangchenggang, Guangxi, and Maoweihai Provincial Nature Reserve in Qinzhou, Guangxi). According to th...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/02G06F17/14
CPCG06F17/14G06Q10/06393G06Q50/02
Inventor 熊庆宇王楷梁山陆旺姚政朱奇武余星刘通
Owner CHONGQING UNIV
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