LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by ...
The LSTM network is an improved structure of a recurrent neural network (RNN), which is a feed forward network with a feedback loop and internal memory56. When using low-quality data for the training process, the RNN can use its own structure to deal with these shortcomings57. As an impr...
植被对于地球生物圈和大气循环具有重要意义,因此获取及时,准确的植被参数信息显得尤为重要.而归一化植被指数(Normalized Difference Vegetation Index,NDVI)作为最常用的一种植被参数,可以有效反映植被生长状态信息,植被生物量等.目前获取NDVI的方式主要是通过光学遥感影像数据,虽然可以取得较好的反演精度,但却存在着易受大气...
������LSTmin 和 ������LSTmax 是观测区域内LST的最小值和最大值。
������LSTmin 和 ������LSTmax 是观测区域内LST的最小值和最大值...
但是LSTM只是针对于一维时序数据,无法对空间数据进行预测分析。XingjianShi等针对该不足提出了卷积长短期记忆网络模型,该模型可以直接对空间数据进行处理,实验证明该算法在时空上解决了临近降水预报的问题,在捕获时空相关性方面具有较好的效果。 ConvLSTM具有直接处理空间...
LSTmax=c+d×NDVI 其中a 、 b 、 c 、 d 为干、湿边拟合系数; 2、Python代码 import gdal from gdalconst import * import numpy as np from glob import glob from os import path as osp import os, subprocess import matplotlib.pyplot as plt # 获取lst、ndvi数据 def get_data(file_ndvi,file_...
Assuming that the MODIS VI data of high quality represents the true values, the root mean square error (RMSE) for NDVI and EVI generated by the LSTM model are 0.0734 and 0.0509, respectively.Xiong, ChanghaoMa, HanLiang, ShunlinHe, Tao...
Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N ...
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