Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to stu...

and Applied Analysis 3

An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. For this purpose, accurate simulation models are mandatory, because the underlying biochemical processes are very slow. The simulation models may be time-co...

We present a methodology to perform spatial prediction when measured data are curves. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterium used in multivariable spatial prediction in order to estimate the kriging parameters. A real data example on soil penetration resistences illustrates our proposals.

Chaotic particle swarm optimization (CPSO) algorithm is proposed to optimize the Kriging model, which can improve the precision of curve fitting. A typical example is selected to demonstrate the advantage of the optimized Kriging model, compared with other curve fitting tools.

Abstract An accurate analysis of spatial rainfall distribution is great importance for managing watershed water resources, in addition to giving support meteorological studies and agricultural planning. This work compares the performance two interpolation methods: Inverse distance weighted (IDW) Kriging, annual distribution. We use data state Rio Grande do Sul (Brazil) from 1961 2017. To determ...

Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algor...

The method of stochastic simulation is proposed to model the atmospheric effect on InSAR measurements based on sample data. Test results show that 37.44% reduction in the standard devisation of the atmospheric errors can be achieved with the method of stochastic simulation, compared to 25.69% with the method of Kriging interpolator. The relative improvement of the former over the latter amounts...