Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

Deep learning (DL) has captured the attention of the community with an increasing number of recent papers in regression applications, including surveys and reviews. Despite the efficiency and good accuracy in systems with high-dimensional data, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. This paper aims to investigate the state-of-the-art of existing deep fuzzy systems (DFS) for regression, i.e., methods that combine DL and FLS with the aim of achieving good accuracy and good interpretability. Within the concept of explainable artificial intelligence (XAI), it is essential to contemplate interpretability in the development of intelligent models and not only seek to promote explanations after learning (post hoc methods), which is currently well established in the literature. Therefore, this work presents DFS for regression applications as the leading point of discussion of this topic that is not sufficiently explored in the literature and thus deserves a comprehensive survey.

This paper introduces a novel method for distilling knowledge from complex models using fuzzy systems. The complex knowledge comes from a proposed hybrid NFN-LSTM model (teacher) composed of a long short-term memory (LSTM) network coupled to a neo-fuzzy neuron (NFN) structure. The proposed student model, the NFN-MOD, is an explainable Takagi-Sugeno fuzzy model that resembles modular characteristics to mimic the temporal memory of the LSTM part in the teacher model. The NFN-MOD is adaptable across many scenarios, including solo learning (without a teacher), with the estimation of a previously trained teacher, or training in parallel with the teacher. The complexity reduction of the student model is achieved through the pruning of its consequent parameters with the lowest L1-norm. Application of NFN-MOD in industrial case studies (sulfur recovery unit and cement manufacturing process) demonstrates the efficiency of NFN-MOD in distilling complex knowledge from the teacher model NFN-LSTM, with emphasis on parallel training and parameter pruning. Additionally, a novel explainability analysis is introduced, which evaluates the influence of antecedent parameters of the student model in relation to the expected real system output.

Digital transformation of industry has gained emphasis in recent years in academia and industry. Organizations need to be more competitive and efficient and improve their processes and performance to cope with changes in environmental legislation, efficient management of resources and energy, and the trend toward zero waste. These factors have led to the emergence of a new concept. This paper studies data-driven fuzzy-based models for process monitoring focused on Wastewater Treatment Plants (WWTPs). This work aims to study interpretable industrial process monitoring models, which must be easily interpretable by expert process operators. For this purpose, different fuzzy-based models were studied. Exhaustive validations are performed. The studied models employ 16 key variables at 14 different points throughout the waterline of a treatment plant. The learning and testing of each model for every key variable at each involved point use distinct sets of input variables and varied learning model parameters. The impact of the selected input variables and the learning parameters on the model accuracy, and the accuracy versus interpretability tradeoff are analyzed. The best model for each key variable is developed based on the accuracy versus interpretability tradeoff.