Distribution Regularized Regression Framework for Climate Modeling
نویسندگان
چکیده
Regression-based approaches are widely used in climate modeling to capture the relationship between a climate variable of interest and a set of predictor variables. These approaches are often designed to minimize the overall prediction errors. However, some climate modeling applications emphasize more on fitting the distribution properties of the observed data. For example, histogram equalization techniques such as quantile mapping have been successfully used to debias outputs from computer-simulated climate models to obtain more realistic projections of future climate scenarios. In this paper, we show the limitations of current regression-based approaches in terms of preserving the distribution of observed climate data and present a multiobjective regression framework that simultaneously fits the distribution properties and minimizes the prediction error. The framework is highly flexible and can be applied to linear, nonlinear, and conditional quantile models. The paper demonstrates the effectiveness of the framework in modeling the daily minimum and maximum temperature as well as precipitation for climate stations in the Great Lakes region. The framework showed marked improvement over traditional regression-based approaches in all 14 climate stations evaluated.
منابع مشابه
Contour regression: A distribution-regularized regression framework for climate modeling
Regression methods are commonly used to learn the mapping from a set of predictor variables to a continuousvalued target variable such that their prediction errors are minimized. However, minimizing the errors alone may not be sufficient for some applications, such as climate modeling, which require the overall predicted distribution to resemble the actual observed distribution. On the other ha...
متن کاملModeling Current and Future Potential Distributions of Caspian Pond Turtle (Mauremys caspica) under Climate Change Scenarios
Although turtles are the most threatened taxonomic group within the reptile class, we have a very limited understanding of how turtles respond to climate change. Here, we evaluated the effects of climate changes on the geographical distribution of Caspian pond turtle (Mauremys caspica). We used an ensemble approach by combining six species distribution models including artificial neural network...
متن کاملEfficient Multiclass Implementations of L1-Regularized Maximum Entropy
This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditional distributions. Furthermore, unlike most previous studies, the present research goes beyond a single type of conditional distribution. It describes and compares ...
متن کاملThe food-energy-water nexus: A framework for sustainable development modeling
Energy, water, and food are facing present and future challenges triggered by climate change, population growth, human behavior, and economics. Management strategies for energy, water, and food are possible through policies, technology, and related education. However, the links between resources (energy, water, and food) and impacting factors (population increase, human behavior, economics, and...
متن کاملApplication of Driving force- Pressure- State- Impact- Response (DPSIR) framework for integrated environmental assessment of the climate change in city of Tehran
Climate change is a complicated issue with many factors playing role in its formation and distribution. Considering this complication, a comprehensive and holistic approach is needed for a better understanding and management of those factors. The causal frameworks are among systemic and integrated methods for addressing the causes of environmental problems and the relationships that exist betwe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013