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.

Dimensionality reduction for groundwater forecasting under drought and intensive irrigation with neural networks

Domaine:

environment and energyclimate

Type de record:

paper
Créateur:
BouMohKarLeb
Éditeur:
UniAviGesLab
Éditeur:
CCSDElsevier
Hôte:avatar
International audience This study focuses on the Berrechid aquifer system in northern Morocco. Study Focus: The research explores Principal Component Analysis (PCA) for optimizing input selection in groundwater level forecasting using neural networks. PCA efficiently reduces input dimensionality while preserving critical information, making it beneficial for neural network modelling of natural systems with extensive input variables in a low-resource scenarios requiring feature engineering. A Long Short-Term Memory (LSTM) model predicted groundwater levels in six monitoring bores using four hydro-climatic variables, precipitation, land surface temperature (LST), actual evapotranspiration (AET), and the normalized difference vegetation index (NDVI). Model performance was compared using two approaches: the LSTM-XGB model with the bestselected input features and the LSTM-PC1 model based on the first principal component (PC1). New Hydrological Insights for the Region: Results showed that NDVI, AET, and LST were the dominant inputs across different monitoring bores. On average, PC1 accounted for 68.3 % of the variance in hydro-climatic variables, with an eigenvalue of 2.75, surpassing the combined variance of two individual hydro-climatic variables. Both models performed effectively, achieving R² values of 0.982-0.999 during training and 0.885-0.999 during validation. The models successfully captured groundwater fluctuations and the declining trend during drought. LSTM-XGB slightly outperformed LSTM-PC1 in certain cases, but the differences were minimal. The use of PC1 not only mitigates overfitting risks but also allows for generalized predictions across multiple monitoring sites, making it a practical choice for large datasets.

Visit

hal.science

Tags

Principal Component Analysis (PCA)Groundwater Level ForecastingSemi-aridLong short-term memoryXGBoostMorocco[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology[SDE.MCG]Environmental Sciences/Global Changes

Licenses

https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/OpenAccess

Similaires

Spatiotemporal drought forecasting over Morocco with recurrent neural networks and domain adaptationStandard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector RegressionDimensionality reduction with missing values imputationOptimizing Weather Forecasting Accuracy via Radial Basis Function Networks, Convolutional Neural Networks and Convolutional Neural NetworksPrediction intervals for electricity load forecasting using neural networksSub-seasonal prediction of drought over the Horn of Africa with Neural Networks

Spatiotemporal drought forecasting over Morocco with recurrent neural networks and domain adaptation

Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of d

Dimensionality reduction with missing values imputation

In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous

Optimizing Weather Forecasting Accuracy via Radial Basis Function Networks, Convolutional Neural Networks and Convolutional Neural Networks

Weather forecasting is crucial for various sectors, including agriculture, disaster management, and

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on i

Sub-seasonal prediction of drought over the Horn of Africa with Neural Networks

Socioeconomic livelihoods in the Horn of Africa (HA) a