Suchergebnisse

Open burning is often used to remove crop residue during the harvest season. Despite a series of regulations by the Chinese government, the open burning of crop residue still frequently occurs in China, and the monitoring and forecasting crop fires have become a topic of active research. In this paper, crop fires in Northeastern China were forecasted using an artificial neural network (ANN) based on moderate-resolution imaging spectroradiometer (MODIS) satellite fire data from 2013–2020. Both natural factors (meteorological, soil moisture content, harvest date) and anthropogenic factors were considered. The model's forecasting accuracy under natural factors reached 77.01% during 2013–2017. When considering the influence of anthropogenic management and control policies, such as the straw open burning prohibition areas in Jilin Province, the accuracy of the forecast results for 2020 was reduced to 60%. Although the forecasting accuracy was lower than for natural factors, the relative error between the observed fire points and the back propagation neural network (BPNN) forecasting results was acceptable. In terms of influencing factors, air pressure, the change in soil moisture content in a 24 h period and the daily soil moisture content were significantly correlated with open burning. The results of this study improve our ability to forecast agricultural fires and provide a scientific framework for regional prevention and control of crop residue burning.

Recently, we proposed an original statistical model for forecasting general elections in the United Kingdom, based on the observation of a few key indicators of the political & economic system. That vote function model was tested against the results of the 2001 general election. Here we evaluate the results of that test, & offer an appropriately revised model for the forecasting of the upcoming 2005 general election. According to our forecast, a Labor victory appears the most likely outcome. 2 Tables, 8 References. Adapted from the source document.

Abstract
Traditional ways of reading nature's clues to figure out impending weather are widely practiced in many rural societies in the world. They have been, however, often discounted by a western science-based meteorological forecasting system, although they are important sociocultural tools for mitigating climatic risks. This paper concerns two thematic issues: traditional knowledge about reading nature's clues to figure out impending weather; and the transformation of that knowledge in the changing context of livelihood, the intervention of modern education, and use of modern weather forecasting technology. This study was carried out at Kirtipur of the Kathmandu Valley, Nepal, between 2011 and 2021. Information was collected through key informant interviews, observation, and informal discussion and survey. The findings reveal that the traditional weather forecasting system is closely intertwined with climatic phenomena, traditional agriculture practices, the local landscape, myths, and beliefs. The recognition of farmers' knowledge on weather forecasting should be a resource of a great potential value. However, rapid expansion of the market economy, access to modern technology, affordability, access to modern education, and anthropogenic climate change have gradually detached people from their farmlands, traditional livelihoods, and occupations. All of this seems to have weakened social interaction between generations as well as their attachment to nature. Last, we conclude that the production and consumption of weather forecasting knowledge need local and scientific communities to work together to reduce knowledge gaps.

Significance Statement
The purpose of this study is to understand how farmers read nature to figure out impending weather, even as such knowledge is gradually weakening with the expansion of new technology, the intervention of modern education, the shift from farming-based to nonfarming activities for livelihood, and climate change. In such a context, the survival of traditional weather forecasting knowledge may seem uncertain. Our study reveals that traditional weather forecasting knowledge is rooted in the local landscape and subsistence farming culture. Therefore, the production and consumption of weather forecasting knowledge need local and scientific communities to work together to reduce knowledge gaps and recognize the contributions of both types of knowledge.

Bayesian econometrics : past, present, and future / Arnold Zellner -- Fitting and comparison of models for multivariate ordinal outcomes / Ivan Jeliazkov, Jennifer Graves, Mark Kutzbach -- Intra-household allocation and consumption of WIC-approved foods : A Bayesian approach / Ariun Ishdorj, Helen H. Jensen, Justin Tobias -- Causal effects from panel data in randomized experiments with partial compliance / Siddhartha Chib, Liana Jacobi -- Parametric and nonparametric inference in equilibrium job search models / Gary Koop -- Do subsidies drive productivity? A cross-country analysis of Nordic dairy farms / Nadine McCloud, Subal C. Kumbhakar -- Semiparametric Bayesian estimation of random coefficients discrete choice models / Sylvie Tchumtchoua, Dipak K. Dey -- Bayesian econometrics : an introduction / Siddhartha Chib, William Griffiths, Gary Koop, Dek Terrell -- Bayesian two-stage regression with parametric heteroscedasticity / Arto Luoma, Jani Luoto -- Bayesian near-boundary analysis in basic macroeconomic time-series models / Michiel de Pooter, Francesco Ravazzolo, Rene Segers, Herman K. van Dijk -- Forecasting in vector autoregressions with many predictors / Dimitris Korobilis -- Bayesian inference in a cointegrating panel data model / Gary Koop, Roberto Leon-Gonzalez, Rodney Strachan -- Investigating nonlinear purchasing power parity during the post-Bretton Woods era : a Bayesian exponential smooth transition VECM approach / Deborah Gefang -- Bayesian forecast combination for VAR models / Michael K. Andersson, Sune Karlsson -- Bayesian inference on time-varying proportions / William J. McCausland, Brahim Lgui -- Imposing stationarity constraints on the parameters of ARCH and GARCH models / Christopher J. ODonnell, Vanessa Rayner -- Bayesian model selection for heteroskedastic models / Cathy W.S. Chen, Richard Gerlach, Mike K.P. So -- Bayesian student-t / S.T. Boris Choy, Wai-yin Wan, Chun-man Chan -- Bayesian inference using adaptive sampling / Paolo Giordani, Robert Kohn -- Bayesian analysis of the consumption CAPM / Veni Arakelian, Efthymios G. Tsionas -- A Bayesian analysis of the OPES model with a nonparametric component: An application to dental insurance and dental care / Murat K. Munkin, Pravin K. Trivedi

Decision theory was developed to prescribe the consistent choice behavior under uncertainty of an ideal individual. The policy analysis context of organizational decision making, however, necessitates certain modifications to the fundamental decision theory in order to deal with the problems of group consensus, ill-defined objectives, & disparate information sources. The argument is presented that more attention must be given to the explicit formulation of judgment, & that, in particular, there has to be an integration of the methodology of forecasting into the decision-analytic framework. A consequence of adopting a decision-theoretic approach to forecasting is the apparent need to develop ways of synthesizing the set of available predictive methods in order to satisfy the subjectivist principle of total evidence utilization. The synthetic approach to forecasting is in contrast to the conventional selective method. A brief review of some operational methods of pursuing the synthetic approach is given. HA.

Part 1: GIS, GPS, RS and Precision Farming ; International audience ; In the course of analyzing and forecasting regional groundwater depth, its variation process complexity was ignored in the past, so that it was difficult to realize scientific management of groundwater resources. Aiming at the aforesaid defect, taking Jiansanjiang branch bureau as research platform, this paper diagnosed the complexity of each monthly groundwater depth series through using complexity measurement methods which include wavelet theory, fractal theory, Approximate Entropy and so on. The comprehensive measurement analysis results show that the complexity order of monthly groundwater depth series in the eighth district of Farm Nongjiang is highest in four farms of northern region. On that basis, the most complexity groundwater depth series combination stochastic forecasting model based on wavelet transform in northern region of Jiansanjiang branch bureau was set up. The model forecasting results show that local annual mean groundwater depth will be in a state of fluctuant lifting, and have considerable gap in comparison to groundwater ideal depth in the future. So, local government should reinforce the scientific management of groundwater. The research achievements break through the traditional study paradigm of hydrological forecasting, provide a research mode for studying regional hydrologic process, and provide scientific evidence for sustainable utilizing regional groundwater resources.

The decarbonization and the climate change mitigation have become a priority for many countries and governments. One of the main tools for accomplishing these objectives is the growth of renewable generation sources in the power system, but their inclusion constitutes a great challenge for the network operation due to their high variability and their stochastic behavior. In this context, the management of the power system and microgrids can be treated as optimization problems in which the resources are operated with the aim of minimizing the cost function. This cost function and the corresponding operative restrictions depend on each specific situation, for example, on which are the power consumption requirements, how weak is the connection with the power grid, and how critical are the loads to be fed in the zone. In this sense, despite the large variety of optimization approaches, these have in common the importance of counting on a high-quality forecasting system for predicting the uncertainties of the microgrid (or network) to operate. The main existing approaches for predicting the uncertainties are deterministic and stochastic (which in many cases is also called probabilistic) forecasting. Considering the importance of forecasting systems for performing the optimization of microgrids and, in general, power networks, this doctoral thesis is focused on the design of a microgrid-oriented forecasting framework that includes a wide range of forecasting approaches, which makes possible its integration with other applications, for example, energy management optimization systems. This framework includes several deterministic and stochastic methods and is able to handle the training and selection of the models for performing the forecast according to the type of uncertainty representation that is required in each case. ; La descarbonización y la reducción del cambio climático se han convertido en una prioridad para muchos países y gobiernos. Una de las principales herramientas para lograr estos objetivos es aumentar ...

"The editors of this collection of sixteen articles argue the relationship between the United States and Mexico is at its most tenuous in recent memory. Each article explores the future of US-Mexico relations, focusing on relevant topics such as trade, water, drugs, health, immigration, environmental issues and security. Employing a strategic foresight methodology, the authors use past trends and identify pivotal drivers to predict, based on indicators, at least three possible outcomes for the next few decades: a baseline or continuity scenario, an optimistic version and a pessimistic one. They also articulate the implications each forecast has for both nations. Most chapters are co-written by a scholar from the United States and another from Mexico. While acknowledging it is impossible to predict the future, they nonetheless describe what could occur. Ultimately, the authors of the articles in this fascinating volume make recommendations to achieve a peaceful, integrated and prosperous North America that will drive the world economy. The book is required reading for anyone interested in the binational relationship and the well-being of citizens in both countries--

"What if Atlantis wasn't a myth, but an early precursor to a new age of great flooding? Across the globe, scientists and civilians alike are noticing rapidly rising sea levels, and higher and higher tides pushing more water directly into the places we live, from our most vibrant, historic cities to our last remaining traditional coastal villages. With each crack in the great ice sheets of the Arctic and Antarctica, and each tick upwards of Earth's thermometer, we are moving closer to the brink of broad disaster. By century's end, hundreds of millions of people will be retreating from the world's shores as our coasts become inundated and our landscapes transformed. From island nations to the world's major cities, coastal regions will disappear. Engineering projects to hold back the water are bold and may buy some time. Yet despite international efforts and tireless research, there is no permanent solution-no barriers to erect or walls to build-that will protect us in the end from the drowning of the world as we know it. The Water Will Come is the definitive account of the coming water, why and how this will happen, and what it will all mean. As he travels across twelve countries and reports from the front lines, acclaimed journalist Jeff Goodell employs fact, science, and first-person, on-the-ground journalism to show vivid scenes from what already is becoming a water world"--

Este informe proporciona cifras actualizadas sobre el desempleo juvenil a nivel mundial y regional. El informe analiza además las tasas de trabajadores pobres, las oportunidades de trabajo decente tanto en las economías desarrolladas como en las economías en desarrollo, así como las desigualdades de género y las tendencias en materia de migración entre los jóvenes

WHAT IS LATENT DEMAND AND THE P.I.E.?The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnin