Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Evaluation of Stochastic and Artificial Neural Network Models for Multi-step Lead Forecasting of NDVI

Domaine:

geospatialenvironment and energy

Type de record:

paper
Créateur:
MwaHaj
Éditeur:
IOP
Hôte:
Abstract Vegetation degradation is associated with human activities and climate change leading to ecosystem changes and biodiversity losses. To reduce the impacts of vegetation degradation, forecasting of vegetation condition is vital in formulating measures to prevent and reduce the losses. Vegetation indices (VI) obtained from remote sensing data, such as the normalized difference vegetation index (NDVI) are widely used to monitor and forecast vegetation condition. In the present study, a stochastic and artificial neural network (ANN) models were compared in modeling and multi-step lead forecasting of NDVI in the Middle Tana River Basin (MTRB), Kenya. Pixel-wise NDVI data for the period 2000 - 2019 was extracted from the MOD13Q1 product of the Moderate Resolution Imaging Spectroradiometer (MODIS). Time lags of NDVI was used as inputs for the models. The results showed that the ANN model outperforms the stochastic model, with a predicting accuracy of RMSE of 0.07207, MSE of 0.00589 and MAE of 0.06417. The multi-step lead forecasting produced satisfactory results indicating the suitability of the models as tools in forecasting NDVI.

Visit

doi.org

Licenses

http://creativecommons.org/licenses/by/3.0/https://iopscience.iop.org/info/page/text-and-data-mining

Similaires

Forecasting scope creep in Egyptian construction projects: an evaluation using Artificial Neural Network (ANN) and Random Forest modelsForecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression ModelsMulti‐Step Forecasting of Wind Speed and Solar Irradiance Using Lag‐Optimized Stochastic Stacked LSTM NetworksAN EVALUATION OF CLASSIFICATION PERFORMANCE OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION AS LOAN SCORING MODELSEvaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in NigeriaARTIFICIAL NEURAL NETWORK MODEL FOR AIR POLLUTION FORECASTING IN KADUNA, NIGERIA

Forecasting scope creep in Egyptian construction projects: an evaluation using Artificial Neural Network (ANN) and Random Forest models

Scope creep is a common problem in construction projects, often leading to cost overruns, delays,

Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models

Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in

Multi‐Step Forecasting of Wind Speed and Solar Irradiance Using Lag‐Optimized Stochastic Stacked LSTM Networks

ABSTRACT The rapid expansion of solar PV and wind generation intensifies the cha

AN EVALUATION OF CLASSIFICATION PERFORMANCE OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION AS LOAN SCORING MODELS

Purpose: Application of credit risk evaluation techniques has continued to receive more research at

Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria

The accurate assessment of water availability throughout the cassava cropping season (the initial, d

ARTIFICIAL NEURAL NETWORK MODEL FOR AIR POLLUTION FORECASTING IN KADUNA, NIGERIA

The goal of air quality forecasting is to predict when air pollution concentrations will reach level