In several places around the world, recent years have been marred by a series of indoor and outdoor fires; forest fire pose the greatest danger. This disaster affects our environment and our lives. It's often possible to avoid similar calamities, if the detection of the fire outbreak is fast enough, reliable and even early. Thanks to new technologies, there's an opportunity to take a measures and observe various phenomena continuously. In fact, it's becoming essential to develop new approaches based on significant progress in artificial intelligence related to images and videos processing. This paper describe some studies in related with deep learning algorithms of AI based on forest fires detection using satellite images.We begin by introducing various deep learning methods commonly used for forest fire detection. Following this, we review popular satellite image datasets utilized in this field. Finally, we present a comparative study of different deep learning methods based on existing research and studies on forest fire and smoke detection and prediction.
The proper operation of water distribution systems (WDS) is essential to the correct functioning of cities and the well-being of their inhabitants. Hence, faults in these systems threaten the continuity and quality of the vital water supply while representing a significant economic and environmental issue, as water losses due to pipe breaks and leakages are substantial worldwide. Data-driven and machine-learning approaches for fault detection and diagnosis (FDD) have proven to be helpful tools to help manage WDS, providing reliable monitoring capabilities. The academic literature shows the emerging relevance of such methods. As for the Colombian context, a literature review shows a need for such applications in real distribution systems. This research proposed a novel FDD method based on PCA and RF, which can capture high-order process dynamics and provide a reliable monitoring capacity. The method feeds on the process sensor signals and augments the input data matrix by including the variables’ time differences. The proposed method was validated on a real WDS in Barranquilla, Northern Colombia. The method was assessed using four major historical datasets. Validation results showed that the method can accurately detect mild and severe faults while controlling for the false alarm rate (about 3%) when monitoring non-faulty operation conditions. Furthermore, the method achieved a robust diagnostic accuracy (99.57%) with the validation data. The proposed method's performance for monitoring tasks demonstrates its effectiveness, constituting a valuable tool to aid operators in real-time early FDD. Future research will focus on including multi-source data (other than the currently available sensor signals) and the effects of time-drifting and process aging.
Due process can be understood as a norm of fundamental law and a positive provision intended to preserve legal certainty; it can also be seen as a jurisdictional institution aimed at configuring procedural guarantees within a judicial action. But its relationship with artificial intelligence takes on a special meaning in the modern constitutional states, in the guarantee constitutions, and in the so-called constitutions of principles, all of these of great influence and gestation in the Euro-pean constitutionalism of the second post-war period, and which have poured their reception in Latin American legal systems and therefore in its fundamental normative bodies. In this paper we will precisely develop the hermeneutic-procedural impact on the administration of justice, of the relationship and connection between due process and artificial intelligence, a topic that will be addressed in the key of theory and philosophy of law. In particular, we will respond specifically to three points of our presentation: (i). What is artificial intelligence ap-plied to law? Theoretical and conceptual perspectives for a new exper-imental practical reason. (ii). Judges and the administration of justice mediated by AI: new meta-normative systems and their judicial her-meneutics. and (iii). Algorithmic jurisprudence: The artificial judicial decision and its procedural impact on the administration of justice.
