Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as t...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 12; S. 2978
Hauptverfasser:	Zhu, Xueqi, Zhu, Wantian, Guo, Lin, Ke, Yinghai, Li, Xiaojuan, Zhu, Lin, Sun, Ying, Liu, Yaxuan, Chen, Beibei, Tian, Jinyan, Gong, Huili
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2023
Schlagworte:	Accuracy Agricultural production Algorithms Artificial intelligence Back propagation Back propagation networks back-propagation neural network China Comparative analysis Compressibility Computer simulation Digital mapping Distribution Geology Google Earth Engine Groundwater Groundwater data Groundwater levels Infrared radiometers Interferometric synthetic aperture radar interferometry Internet Land subsidence Learning algorithms Machine learning Measurement multiscale Neural networks Optimization Passeriformes Radiometry Remote sensing Root-mean-square errors Search algorithms Sensitivity analysis Simulation Simulation models sparrow search algorithm Spatial analysis Spatial data Subsidence subsidence simulation Subsidences (Earth movements) Synthetic aperture radar Thickness time series analysis water table Weighting China Beijing China Italy Mexico Iran Indonesia
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!