Association Rule Algorithm Sequential Pattern Discovery using Equivalent Classes (SPADE) to Analyze the Genesis Pattern of Landslides in Indonesia

Landslide is one of movement of soil, rock, soil creep, and rock debris that occurred the move of the slopes. It is caused by steep slopes, high rainfall, deforestation, mining activities, and erosion. The impacts of the landslide are loss of property, damage to facilities such as homes and buildings, casualties, psychological trauma, disrupted economic and environmental damage. Based on the impacts of landslide, mitigation required to take early precautions are to know how the pattern of association between the sequence of events landslides and to know how the associative relationship pattern of earthquakes. Based on the impacts, the results of this research is associative relationship pattern is obtained from data flood that occurs in Indonesia, namely in case of heavy rain will occur labile soil structure to support the value of 0.37, confidence level of 41% and the power of formed ruled is 1.02

Advancing cumulative effects assessment methodology for river systems

Increased land use intensity has adversely affected aquatic ecosystems within Canada. Activities that occur over the landscape are individually minor but collectively significant when added to other past, present, and reasonably foreseeable future actions, and are defined as cumulative effects. Existing approaches to cumulative effects assessment for river systems within Canada are ineffective. This thesis aims to improve the practice of cumulative effects assessment by evaluating current methodology for linking landscape change and river response over a large spatiotemporal scale. As part of this goal, I offer a framework for better incorporating science into current practices for cumulative effects assessment. The framework addresses the challenges involved in cumulative effects assessment, such as defining appropriate spatial and temporal scale, complex ecological and hydrologic pathways, predictive analysis, and monitoring. I then test the framework over a large spatiotemporal scale using a case study of the lower reaches of the Athabasca River Basin, Alberta. Three objectives are addressed: 1) changes in land use and land cover in the lower ARB for several census dates (1981, 1986, 1991, 1996, 2001) between 1976 (historic) and 2006 (current day) are identified; 2) linkages between landscape change and river water quality and quantity response are evaluated; and 3) results of the different methods used to link landscape stressors with stream responses are compared. Results show that the landscape has changed dramatically between 1976 and 2006, documented by increases in forest harvesting, oil sands developments, and agricultural intensity. Secondly, results suggest that linear regression tests combined with regression trees are useful for capturing the strongest associations between landscape stressors and river response variables. For instance, water abstraction and agricultural activities have a significant impact on solute concentrations. This suggests that water abstraction and agriculture are important indicators to consider when conducting a watershed cumulative effect assessment on a similar spatiotemporal scale. The thesis has strong implications for the need for improved water quality and quantity monitoring of Canada‟s rivers. The research provides a means of identifying appropriate tools for improved watershed cumulative effects assessment for scientists and land managers involved in the environmental impact assessment process and protection of Canada‟s watersheds

GIS-based decision support approach for selecting a new landfill site for the city of Cape Town

Includes bibliographical references (leaves 107-111).Recent studies indicate that the population of Cape Town generates approximately 2.2 milliontons of waste annually. Numerous waste minimization strategies have been developed whichhave not been successful in reducing the amount that needs to be disposed of at a landfill site.This results to mounting pressure on existing waste disposal sites thus necessitating an urgentneed for a new regional landfill. According to CCA Draft Environmental Impact Report (2006),the former Cape Metropolitan Council (CMC) appointed technical consultants in 2000 to identifyand assess the potential sites for a landfill to service Cape Metropolitan Area (CMA), presentlyreferred to as the City of Cape Town (CCT). The construction of a landfill has significant impacts on the environments. It is for that reason Integrated Environmental Management (IEM) has to be followed to assess the impacts. The principle of IEM is broadly interpreted as applying to the planning, assessment, implementation and management of any project proposal or activity that has a potentially significant effect on the environment. Environmental Impact Assessment (EIA) process, which lies in the heart of the IEM, is enforced to examine the environmental effects of development. These impacts are directly related to the physical location of the project. That makes site selection for proposed project a very important stage of the EIA process. Laws have been enacted to minimizeenvironmental impacts, including strict guidelines for siting landfills. Using landfill siting criteria and site selection methods, the technical consultants identified four potential sites, Atlantis being the only site falling within the City of Cape Town. The interviews, backed by secondary data sources such as websites and project reports, revealed that the techniques used to identify potential sites for the landfill, even when combined are costly and time consuming. Several scenarios were run using various ArcGIS extensions, including the ModelBuilder to identify sites that met the stated criteria. GIS analysis yielded agreeable results with the recommendations from the consultants who used techniques other than GIS to identify the regional landfill. The research findings demonstrate that GIS is an efficient and dependable stand-alone technique that can be implemented in landfill site studies thus expedite the decision making process

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

Social Indicators for Arctic Mining

This paper reviews and assesses the state of the data to describe and monitor mining trends in the pan-Arctic. It constructs a mining index and discusses its value as a social impact indicator and discusses drivers of change in Arctic mining. The widely available measures of mineral production and value are poor proxies for economic effects on Arctic communities. Trends in mining activity can be characterized as stasis or decline in mature regions of the Arctic, with strong growth in the frontier regions. World prices and the availability of large, undiscovered and untapped resources with favorable access and low political risk are the biggest drivers for Arctic mining, while climate change is a minor and locally variable factor. Historical data on mineral production and value is unavailable in electronic format for much of the Arctic, specifically Scandinavia and Russia; completing the historical record back to 1980 will require work with paper archives. The most critically needed improvement in data collection and reporting is to develop comparable measures of employment: the eight Arctic countries each use different definitions of employment, and different methodologies to collect the data. Furthermore, many countries do not report employment by county and industry, so the Arctic share of mining employment cannot be identified. More work needs to be done to develop indicator measures for ecosystem service flows. More work also needs to be done developing conceptual models of effects of mining activities on fate control, cultural continuity and ties to nature for local Arctic communities

Towards universal health coverage: mapping the development of the faith-based non-profit sector in the Ghanaian health system

The equitable provision of accessible quality health services and the achievement of universal health coverage (UHC) continue to be prominent on the global health agenda, yet remains an elusive target for many low- and middle-income countries (LMIC). In these contexts, the private not-for-profit (PNFP) sector plays a significant role, and in many African countries, faith-based non-profit (FBNP) providers dominate this sector. Robust public-private partnerships are increasingly being recognised as important to building and maintaining strong, resilient health systems. However, there is a lack of evidence on whether collaborations between FBNPs and the public sector are complementary, have achieved their intended aims, or exactly how these relationships developed over time to shape these health systems. Furthermore, reliable information on both the historical and current spatial distribution of services and how this relates to geographic accessibility and the achievement of UHC is limited. This study explores this in Ghana, a country with a large FBNP sector, mostly networked under the Christian Health Association of Ghana (CHAG) which has an influential and now formalised relationship with the government. The following health systems research study utilises a mixed methods approach, synthesising geospatial mapping with varied documentary resources (secondary and primary, current and archival). The evolution of the FBNP sector and the shifts in service footprint are reflected in the geospatial maps, aligned with key historical events and contextualised by a narrative analysis. The study highlights that many faith-based facilities were initially located in rural and remote areas beyond colonial governance control (or boundaries), and many of these facilities still exist, demonstrating resilience to change over time. However, this service footprint has changed and today, public and private health facilities are located in similar areas throughout the country. This trend is in-line with social and political events, changing population dynamics and an increasing population of urban poor. The analysis assesses how the growth of the public sector, and these shifts in presence and profile for the FBNPs has influenced their perceived and measured contribution to UHC - in particular geographic accessibility. This study provides a model for representing the evolution of the relationship between public and a particular type of non-state provider over time, characterising the historical development of the health system, which should be considered in efforts to strengthen and develop the Ghanaian health system, and other relatable LMIC health systems

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal "monolithic" models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 10^{4} participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10^{-2}, MSE ≤ 7 × 10^{-3}, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to - 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways

Strategic environmental assessment practices in European small islands : insights from Azores and Orkney islands

The literature concerning Strategic Environmental Assessment (SEA) often refers to the importance of context-specific approaches. However, there is a lack of systematised and consistent studies that enhance tailor-made SEA practices and procedures. Small islands are bounded units of study which may help explore SEA theory and practice in special territories. Small islands present particular features and unique values, such as, small size and population, geographic isolation, limited resources and vulnerable ecosystems. Hence, the main goal of this research was to profile SEA practices and procedures in European small islands and provide a background for future research aiming to improve context-specific SEA applications. To achieve this goal, an exploratory case study was developed using Azores (Portugal) and Orkney (Scotland) archipelagos. An analysis of the corresponding mainland was also carried out to contextualise both case studies. The data collection was achieved through a qualitative content analysis of 43 Environmental Reports. The research found that there is not an SEA context-specific approach used within these European small islands, including guidelines, assessment topics, assessment techniques, follow-up and stakeholders engagement. The debate concerning specific approaches to small islands must be re-focused on the enhancement of SEA capacity-building amongst different stakeholders (including decision-makers), on the development and implementation of collaborative approaches, and on the exchange of knowledge and experiences between small islands networks

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