Search results for: Sadia Zaman

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Z. Zaman Asam, M. Ajmal, R. Saeed, H. Miraj, M. Muhammad Ahtisham, B. Hameed, A. -Sattar Nizami

Abstract:

In Pakistan, environmental degradation and consequent human health deterioration has rapidly accelerated in the past decade due to solid waste mismanagement. As the situation worsens with time, establishment of proper waste management practices is urgently needed especially in semi urban and rural areas of Pakistan. This study uses a concept of Waste Bank, which involves a transfer station for collection of sorted waste fractions and its delivery to the targeted market such as recycling industries, biogas plants, composting facilities etc. The management efficiency and effectiveness of Waste Bank depend strongly on the proficient sorting and collection of solid waste fractions at household level. However, the social attitude towards such a solution in semi urban/rural areas of Pakistan demands certain prerequisites to make it workable. Considering these factors the objectives of this study are to: [A] Obtain reliable data about quantity and characteristics of generated waste to define feasibility of business and design factors, such as required storage area, retention time, transportation frequency of the system etc. [B] Analyze the effects of various social factors on waste generation to foresee future projections. [C] Quantify the improvement in waste sorting efficiency after awareness campaign. We selected Gujrat city of Central Punjab province of Pakistan as it is semi urban adjoined by rural areas. A total of 60 houses (20 from each of the three selected colonies), belonging to different social status were selected. Awareness sessions about waste segregation were given through brochures and individual lectures in each selected household. Sampling of waste, that households had attempted to sort, was then carried out in the three colored bags that were provided as part of the awareness campaign. Finally, refined waste sorting, weighing of various fractions and measurement of dry mass was performed in environmental laboratory using standard methods. It was calculated that sorting efficiency of waste improved from 0 to 52% as a result of the awareness campaign. The generation of waste (dry mass basis) on average from one household was 460 kg/year whereas per capita generation was 68 kg/year. Extrapolating these values for Gujrat Tehsil, the total waste generation per year is calculated to be 101921 tons dry mass (DM). Characteristics found in waste were (i) organic decomposable (29.2%, 29710 tons/year DM), (ii) recyclables (37.0%, 37726 tons/year DM) that included plastic, paper, metal and glass, and (iii) trash (33.8%, 34485 tons/year DM) that mainly comprised of polythene bags, medicine packaging, pampers and wrappers. Waste generation was more in colonies with comparatively higher income and better living standards. In future, data collection for all four seasons and improvements due to expansion of awareness campaign to educational institutes will be quantified. This waste management system can potentially fulfill vital sustainable development goals (e.g. clean water and sanitation), reduce the need to harvest fresh resources from the ecosystem, create business and job opportunities and consequently solve one of the most pressing environmental issues of the country.

Keywords: integrated solid waste management, waste segregation, waste bank, community development

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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