Regional Hazard Prediction Model Based on LSTM Recurrent Neural Network and TOPSIS Algorithm - Details

author：

Shi, Yuxuan (Shi, Yuxuan.) ^[1] | Lin, Zhanmin (Lin, Zhanmin.) ^[2] | Lin, Binru (Lin, Binru.) ^[3]

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EI Scopus

Abstract：

This　paper　introduces　a　Real　Estate　Development　Evaluation　Model　that　integrates　advanced　predictive　and　evaluative　techniques　to　prioritize　areas　for　real　estate　development.　Initially,　Long　Short-Term　Memory　(LSTM)　models　are　employed　to　forecast　the　incidence　of　disasters　in　various　regions　over　the　next　decade.　Subsequently,　the　Technique　for　Order　of　Preference　by　Similarity　to　Ideal　Solution　(TOPSIS)　method　is　utilized　to　conduct　a　comprehensive　evaluation　based　on　multiple　indicators,　with　the　goal　of　assessing　the　suitability　of　construction　and　development　in　each　area.　As　a　result,　ten　priority　areas　are　identified,　with　North　Carolina　emerging　as　a　key　region　for　real　estate　development.　©　2024　IEEE.

Keyword：

Long short-term memory Prediction models

Community：

[ 1 ] [Shi, Yuxuan]School of Mathematics and Statistics, Fuzhou University, Fuzhou, China
[ 2 ] [Lin, Zhanmin]School of Mathematics and Statistics, Fuzhou University, Fuzhou, China
[ 3 ] [Lin, Binru]School of Mathematics and Statistics, Fuzhou University, Fuzhou, China

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Regional Hazard Prediction Model Based on LSTM Recurrent Neural Network and TOPSIS Algorithm
2024，2024 IEEE 2nd International Conference on Image Processing and Computer Applications, ICIPCA 2024

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Source ：

Year： 2024

Page： 1855-1859

Language： English

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