اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدForecasting vegetation condition for drought early warning systems in pastoral communities in Kenya
46
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs.Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost. However, existing EWS tend only to monitor current, rather than forecast future, environmental and socioeconomic indicators of drought, and hence are not always sufficiently timely to be effective in practice. Here we present a novel method for forecasting satellite-based indicators of vegetation condition. Specifically, we focused on the 3-month Vegetation Condition Index (VCI3M) over pastoral livelihood zones in Kenya, which is the indicator used by the Kenyan National Drought Management Authority(NDMA). Using data from MODIS and Landsat, we apply linear autoregression and Gaussian process modeling methods and demonstrate high forecasting skill several weeks ahead. As a benchmark we predicted the drought alert marker used by NDMA (VCI3M<35). Both of our models were able to predict this alert marker four weeks ahead with a hit rate of around 89% and a false alarm rate of around 4%, or 81% and 6% respectively six weeks ahead. The methods developed here can thus identify a deteriorating vegetation condition well and sufficiently in advance to help disaster risk managers act early to support vulnerable communities and limit the impact of a drought hazard.
قيم البحث
اقرأ أيضاً
In this review, we present the different measures of early warning signals that can indicate the occurrence of critical transitions in complex systems. We start with the mechanisms that trigger critical transitions, how they relate to warning signals
and the methods used to detect early warning signals (EWS) for sudden transitions or tipping. We discuss briefly a few applications in real systems in this context, like transitions in ecology, climate and environment, medicine, epidemics, finance and engineering. Towards the end, we mention the issues in detecting EWS in specific applications and our perspective on future trends in this area, especially related to sudden transitions in the dynamics of connected systems on complex networks.
The economic consequences of drought episodes are increasingly important, although they are often difficult to apprehend in part because of the complexity of the underlying mechanisms. In this article, we will study one of the consequences of drought
, namely the risk of subsidence (or more specifically clay shrinkage induced subsidence), for which insurance has been mandatory in France for several decades. Using data obtained from several insurers, representing about a quarter of the household insurance market, over the past twenty years, we propose some statistical models to predict the frequency but also the intensity of these droughts, for insurers, showing that climate change will have probably major economic consequences on this risk. But even if we use more advanced models than standard regression-type models (here random forests to capture non linearity and cross effects), it is still difficult to predict the economic cost of subsidence claims, even if all geophysical and climatic information is available.
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e.g., heterogeneous connectivity, mesoscale organization, hierarchy - affect their robustness to e
xternal perturbations, such as targeted attacks to their units. Identifying the minimal set of units to attack to disintegrate a complex network, i.e. network dismantling, is a computationally challenging (NP-hard) problem which is usually attacked with heuristics. Here, we show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns, allowing to disintegrate large-scale social, infrastructural and technological networks more efficiently than human-based heuristics. Remarkably, the machine assesses the probability that next attacks will disintegrate the system, providing a quantitative method to quantify systemic risk and detect early-warning signals of systems collapse. This demonstrates that machine-assisted analysis can be effectively used for policy and decision making to better quantify the fragility of complex systems and their response to shocks.
This note proposes a penalty criterion for assessing correct score forecasting in a soccer match. The penalty is based on hierarchical priorities for such a forecast i.e., i) Win, Draw and Loss exact prediction and ii) normalized Euclidian distance b
etween actual and forecast scores. The procedure is illustrated on typical scores, and different alternatives on the penalty components are discussed.
Emerging transportation technologies, such as ride-hailing and autonomous vehicles, are disrupting the transportation sector and transforming public transit. Some transit observers envision future public transit to be integrated transit systems with
fixed-route services running along major corridors and on-demand ridesharing services covering lower-density areas. A switch from a conventional fixed-route service model to this kind of integrated mobility-on-demand transit system, however, may elicit varied responses from local residents. This paper evaluates traveler preferences for a proposed integrated mobility-on-demand transit system versus the existing fixed-route system, with a particular focus on disadvantaged travelers. We conducted a survey in two low-resource communities in the United States, namely, Detroit and Ypsilanti, Michigan. A majority of survey respondents preferred a mobility-on-demand transit system over a fixed-route one. Based on ordered logit model outputs, we found a stronger preference for mobility-on-demand transit among males, college graduates, individuals who have never heard of or used ride-hailing before, and individuals who currently receive inferior transit services. By contrast, preferences varied little by age, income, race, or disability status. The most important benefit of a mobility-on-demand transit system perceived by the survey respondents is enhanced transit accessibility to different destinations, whereas their major concerns include the need to actively request rides, possible transit-fare increases, and potential technological failures. Addressing the concerns of female riders and accommodating the needs of less technology-proficient individuals should be major priorities for transit agencies that are considering mobility-on-demand initiatives.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
