Meriame Mohajane

TL;DR: In this article, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987, one Enhanced Thematic Mapper Plus (ETM+) scene from 2000, two Thematic Map Mapper (TM) scenes from 1995 and 2011, and one Landsat 8 Operational Land Imager (OLI) Scene from 2017, were acquired and processed.

TL;DR: In this paper, the authors developed five hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio Logistic Regression (FRL), CART-FR, LR-FR and SVM-SVM for mapping forest fire susceptibility in the north of Morocco.

TL;DR: The results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE and demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach.

TL;DR: The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS, and ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes.

TL;DR: This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem, and found the overall accuracy of classification was around 99.72%.