Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

Abstract

Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove

Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

“…After that, the flow direction and downhill slope of a central pixel to one of eight neighbors was calculated. Then, flow accumulation was calculated followed by deriving major stream networks using a threshold value of 45 [14]. This value was optimal to reveal the major stream networks in the study area.…”

“…Another group, such as altitude, slope, and topographic curvature in the lower part of the regression tree allowed recognition areas of a higher probability of FF occurrence. Among several interval methods, the quantile method, which is used widely in the literature, was chosen to classify FFSM [12,14,36]. The produced FFSM was then classified into four classes namely low, moderate, high, and very high.…”

“…Each metric includes accuracy, precision, recall and F1 score. The F1 score was found to the best technique and used widely in literature [13,14,80]. The F1 score was calculated based on four parameters, namely true positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) using the following equations from 7-11:…”

“…We first used three machine learning models to map the susceptibility of natural phenomena with nonlinear relationships and without the need for prior elimination of statistical supposition and data transformation [12,92,93]. These types of models can fit complex nonlinear relationships between FF locations and conditioning parameters and their efficiency compared based on accuracy matrices (precision, recall and F1 score) and AUC-ROC [14].…”

“…Susceptibility can be defined as a prediction of where the future hazardous event is likely to occur [9,10]. The wide availability of free of charge remote sensing data and machine learning algorithms allowed researchers to susceptibility map and predict flash floods over a regional scale efficiently and economically [11][12][13][14].…”