Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn's disease cases) A preliminary study

We propose to explore the potential of physics-informed neural networks (PINNs) in solving a class of partial differential equations (PDEs) used to model the propagation of chronic inflammatory bowel diseases, such as Crohn's disease and ulcerative colitis. An unsupervised approach was privileged during the deep neural network training. Given the complexity of the underlying biological system, characterized by intricate feedback loops and limited availability of high-quality data, the aim of this study is to explore the potential of PINNs in solving PDEs. In addition to providing this exploratory assessment, we also aim to emphasize the principles of reproducibility and transparency in our approach, with a specific focus on ensuring the robustness and generalizability through the use of artificial intelligence. We will quantify the relevance of the PINN method with several linear and non-linear PDEs in relation to biology. However, it is important to note that the final solution is dependent on the initial conditions, chosen boundary conditions, and neural network architectures

Estimating Forest Fire Losses Using Stochastic Approach: Case Study of the Kroumiria Mountains (Northwestern Tunisia)

Kroumiria Mountains (northwestern Tunisia) have experienced major fires, making them the main loss reason of Tunisian forested areas. The ability of accurately forecasting or modeling forest fire areas may significantly aid optimizing fire-fighting strategies. However, there are still limitations in the empirical study of forest fire loss estimation because the poor availability and low quality of fire data. In this study, a stochastic approach based on Markov process was developed for the prediction of burned areas, using available meteorological data sets and GIS layers related to the forest under analysis. The Self-organizing map (SOM) was initially used to classify spatiotemporal factors influencing the fire behavior. Subsequently, the SOM clusters were incorporated into a Hidden Markov Model (HMM) framework to model their corresponding burned areas. Results achieved using a database of 829 forest fires records between 1985 and 2016, showed the appropriateness of the HMM approach for the prediction of burned areas compared with a state-of-the art machine learning methods. The transition probability matrix (TPM) and the emission probability matrix (EPM) were also analyzed to further understand the spatiotemporal patterns of fire losses

Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia

This study aims to assess the performance of the Sentinel-2 and Landsat 8 sensors to map forest fires. We choose two fire events, the Haddad fire and the Sidi Ferdjani fire, in northwestern Tunisia in 2017. Several spectral indices were derived from each sensor and the performance of each spectral index was assessed. A validation exercise was undertaken for each fire to compare the spatial matching between the burned area retrieved from each spectral index and its homologue obtained from the Emergency Management Service (EMS). Our results indicate that ΔNBR and its relativized version RBR derived from both sensors exhibit the highest discrimination power (M-statistic values >2.5). The Sentinel sensor is slightly more efficient than the Landsat 8 in mapping burned scars, but both sensors produce acceptable results. We conclude that both sensors could be a good alternative to EFFIS data, particularly when there is a need to detect details inside the burned areas

Estimating Forest Losses Using Spatio-temporal Pattern-based Sequence Classification Approach

Consistent forest loss estimates are important to enforce forest management regulations. In Tunisia, recent evidence has suggested that the deforestation rate is increasing, especially since the 2011’s Revolution. However, no spatially explicit data on the extent of deforestation before and after the Revolution exists. Here, we quantify deforestation in the country for the period 2001–2014 and we propose a novel spatio-temporal pattern-based sequence classification framework for forest loss estimation. To do so, expert knowledge and spatial techniques are applied to identify deforestation drivers. Then, we adopt sequential pattern mining to extract sets of patterns sharing similar spatiotemporal behavior. The sequence miner generates multidimensional-closed sequential patterns at different time granularities. Then, a discriminative filter is employed to decide on patterns to use as relevant classification features. Lastly, the classifier is trained using random forest and shows an improved result