TACOP: A Cognitive Agent for a Naval Training Simulation Environment

The full version of this paper appeared in: Doesburg, W. A. van, Heuvelink, A., and Broek, E. L. van den (2005). TACOP: A cognitive agent for a naval training simulation environment. In M. Pechoucek, D. Steiner, and S. Thompson (Eds.), Proceedings of the Industry Track of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-05), p.34-41. July 25-29, Utrecht, The Netherlands

Simulating Growth and Development of Tomato Crop

Crop models are powerful tools to test hypotheses, synthesize and convey knowledge, describe and understand complex systems and compare different scenarios. Models may be used for prediction and planning of production, in decision support systems and control of the greenhouse climate, water supply and nutrient supply. The mechanistic simulation of tomato crop growth and development is described in this paper. The main processes determining yield, growth, development and water and nutrient uptake of a tomato crop are discussed in relation to growth conditions and crop management. Organ initiation is simulated as a function of temperature. Simulation of leaf area expansion is also based on temperature, unless a maximum specific leaf area is reached. Leaf area is an important determinant for the light interception of the canopy. Radiation shows exponential extinction with depth in the canopy. For leaf photosynthesis several models are available. Transpiration is calculated according to the Penman-Monteith approach. Net assimilate production is calculated as the difference between canopy gross photosynthesis and maintenance respiration. The net assimilate production is used for growth of the different plant organs and growth respiration. Partitioning of assimilates among plant organs is simulated based on the relative sink strengths of the organs. The simulation of plant-nutrient relationships starts with the calculation of the demanded concentrations of different macronutrients for each plant organ with the demand depending on the ontogenetic stage of the organ. Subsequently, the demanded nutrient uptake is calculated from these demanded concentrations and dry weight of the organs. When there is no limitation in the availability at the root surface, the actual uptake will equal the demanded uptake. When the root system cannot fulfil the demand, uptake is less, plant nutrient concentration drops and crop production might be reduced. It is concluded that mechanistic crop models accurately simulate yield, growth, development and water and nutrient relations of greenhouse grown tomato in different climate zone

Technical solutions to prevent heat stress induced crop growth reduction for three climatic regions in Mexico

In the last 15 years a significant increase in greenhouse area has occurred in Mexico, from a modest 50 hectares in 1990 to over 2,000 hectares in 2004. The rapid increase in greenhouse area is a result of an attractive export market, USA. Mexican summer midday temperatures are well above crop optimum and cooling is needed if heat stress induced crop growth reduction is to be prevented. The objective of this study was to determine the effectiveness and feasibility of greenhouse cooling systems for tomato culture under desert, humid tropic and temperate Mexican weather conditions. These climate regions are represented by Mexicali, Merida and Huejutla respectively. The cooling systems included a variety of passive and active systems, which through an engineering design methodology were combined to suit the climate conditions of the 3 regions. The evaluation was conducted via simulation, taking into account the most important temperature effects on crop growth and yield. The results showed that the cooling systems were effective in decreasing heat stress to plants. Investment costs of greenhouse with cooling equipment were under USD 50 m-2 and operational costs were under USD 10 m-2 for all equipment combinations and treatments except for the humid tropic climate of Merida. Solutions for Merida were both economically and physically not feasible due to too high humidity levels. This model study clearly indicates that cooling is feasible in desert and moderate climate regions of Mexico but in humid tropic climate regions feasibility is a problem. Application of design methodology and design evaluation with help of simulation greatly contributed to pointing out effective and non-effective solutions to reduce heat stress in hot climates

Cognitive Models for Training Simulations

Treur, J. [Promotor]Bosch, K. [Copromotor]van den Klein, M.C.A. [Copromotor

Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models

In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 × 200m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher uncertainty at lower intensities. Results from this study can be used for uncertainty analyses of hydrologic and hydrodynamic modelling of similar-sized urban catchments as it provides information on uncertainty associated with rainfall estimation, which is arguably the most important input in these models. This will help to better interpret model results and avoid false calibration and force-fitting of model parameters

Random Forest Spatial Interpolation

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made