Comparative study on machine learning algorithms for early fire forest detection system using geodata

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, the Hai Phong city (Vietnam) using GIS-Based Kernel Logistic Regression

-The Cat Ba National Park area (Vietnam) with the tropical forest is recognized to be part of the world biodiversity conservation by United Nations Educational, Scientific and Cultural Oranization (UNESCO) and is a well-known destination for tourist with around 500,000 travellers per year. This area has been the site for many research projects; however no project has been carried out for the forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authority. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authority in the forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the Support vector machine. The results show that the Kernel logistic regression model has high performance on both the training and validation dataset with a prediction capability of 92.2%. Since the Kernel logistic regression model outperform the benchmark model, we conclude that the proposed model is a promising alternative tool that should be considered for forest fire susceptibility mapping also for other areas. The result in this study is useful for the local authority in forest planning and management

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

Analisis Bahaya Kebakaran Hutan dan Lahan di Jalur Pendakian Gunung Merbabu, Gunung Sindoro dan Gunung Sumbing, Jawa Tengah

Kebakaran hutan dan lahan adalah suatu kondisi bahaya yang terjadi, yang melibatkan unsur panas, bahan bakar dan udara / oksigen (lazim disebut segitiga api) yang bisa mengakibatkan kerusakan hutan dan atau hasil hutan yang menimbulkan kerugian ekonomis dan atau nilai lingkungan (Peraturan Menteri Kehutanan, 2009). Kebakaran hutan dan lahan secara garis besar disebabkan oleh 2 faktor yakni faktor alami dan faktor aktivitas manusia yang di luar kontrol dan tidak bertanggungjawab. Pengaruh iklim global El-Nino yang menyebabkan kemarau berkepanjangan membuat vegetasi menjadi kering dan gersang dan sangat gampang terbakar jika terkena percikan api. Di sisi lain, faktor manusia dan kegiatannya yang di luar kontrol terutama yang berkaitan dengan pembakaran sengaja untuk pembukaan lahan juga menjadi faktor penyebab kebakaran hutan dan lahan. Penelitian ini diarahkan untuk mengkaji bahaya (hazard) kebakaran hutan dan lahan dengan pendekatan analisis kuantitatif berupa pembobotan (scoring) dan penampalan (overlay), dengan hasil akhir berupa indeks bahaya kebakaran hutan dan lahan, luasan kelas bahaya administratif dan penyajian peta bahaya kebakaran hutan dan lahan untuk gunung Merbabu, Sindoro dan Sumbing. Pengecekan lapangan dilakukan sebagai upaya supervisi hasil analisis dan kajian agar tersaji kajian bahaya kebakaran hutan dan lahan yang akurat

A Brief Review of Machine Learning Algorithms in Forest Fires Science

Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires

Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires