Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India

Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic and topographical factors, while this research expands the scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), and machine learning (ML) methodologies for identifying and assessing forest fire-prone areas in the STR and their vulnerability to climate change. To achieve this, the study employed a comprehensive dataset of forty-four influencing factors, including topographic, climate-hydrologic, forest health, vegetation indices, radar features, and anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Gradient Boosting Machine (GBM), Random Forest (RF), and its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm Optimization (RF-PSO), and genetic algorithm (RF-GA). The study revealed high FFS in both the northern and southern portions of the study area, with the nnet and RF-PSO models demonstrating susceptibility percentages of 12.44% and 12.89%, respectively. Conversely, very low FFS zones consistently displayed susceptibility scores of approximately 23.41% and 18.57% for the nnet and RF-PSO models. The robust mapping methodology was validated by impressive AUROC (>0.88) and kappa coefficient (>0.62) scores across all ML validation metrics. Future climate models (ssp245 and ssp585, 2022–2100) indicated high FFS zones along the northern and southern edges of the STR, with the central zone categorized from low to very low susceptibility. Boruta analysis identified actual evapotranspiration (AET) and relative humidity as key factors influencing forest fire ignition. SHAP evaluation reinforced the influence of these factors on FFS, while also highlighting the significant role of distance to road, distance to settlement, dNBR, slope, and humidity in prediction accuracy. These results emphasize the critical importance of the proposed approach for forest fire mapping and provide invaluable insights for firefighting teams, forest management, planning, and qualification strategies to address future fire sustainability

Quantum Machine Learning Technique for Automatic Retrosynthetic Reaction Pathway Search Method

Retrosynthetic analysis often involves evaluating many potential candidate reaction pathways and molecules at multiple stages of the reaction, resulting in complex retrosynthesis trees that need to be searched and parsed efficiently. Computational approaches could significantly aid the chemist in  solving different aspects of the retrosynthesis problem, such as the graph-theoretic search methodologies for efficient tree traversal to identify feasible reaction pathways, dictionary-based methods to evaluate a large search space of precursors, and chemistry-driven heuristics to eliminate practically infeasible routes. In this research, a new single-step retrosynthesis prediction method of the Retro TRAE SMILES-based translation technique is proposed. Accordingly, quantum computing with tree-tensor network topology is presented to construct an automatic data-driven end-to-end retrosynthetic route planning system (Auto-Syn-Route), which is presented based on the heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The model is trained in an end-to-end and fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, a new way of representing a chemical reaction based on molecular fragments is introduced. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods. The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. The proposed method is implemented using Python software. The proposed approach predicts highly similar reactant molecules with an accuracy of 68%. In addition, the proposed method yields more robust predictions than existing methods. However, the experiments demonstrate that the proposed scheme significantly improves the success rate of solving the retrosynthetic problem by 97% while maintaining the performance of the quantum tree tensor for predicting valid reactions

Challenges in data-based geospatial modeling for environmental research and practice

With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Improved Methods and Metrics for Assessing Impacts, Vulnerability and Adaptation

Over the course of the MEDIATION project, Work Package 2 was tasked with "develop[ing] and apply[ing] a toolbox, defined as a set of models, methods, and metrics for the assessment of impacts and vulnerability and adaptation options." As highlighted in Deliverable 2.2, many frameworks and methods for assessing adaptation have been developed over the last 20 years, yet these often have not been adopted in the context of formal adaptation policies in Europe and elsewhere. Reasons and problems include: (i) a fragmentation of methods and tools, (ii) a lack of linkages to actual policy needs, (iii) a lack of understanding and communication of uncertainties, (iv) the often expert-based nature and complexity of methods used versus actual user demands, and (v) a lack of consistent data, definitions and metrics. Deliverable 2.2 put forward a rough prototype for a toolbox of methods for studying impacts, vulnerability, and adaptation. In this deliverable, we discuss subsequent work on the MEDIATION toolbox, and report on application and testing of the improved methods and metrics in selected key European sectors and regions. We present feedback and improvement to methods and metrics based on input from case studies, stakeholders, and focus groups, as well as an overview of case study work and contribution to an improved MEDIATION toolbox. This input resulted in a number of conclusions relating to the development and use of methods and metrics, reducing uncertainty in CCIAV, and led to a number of changes, including the creation of a novel typology for classifying methods and models relating to CCIAV analysis. We provide an overview of the new typology, as well as the final toolbox, and summarize case study contributions towards improved methods and metrics