TOWARDS IMPLEMENTATION OF COMPONENT-BASED MODELING IN THE PLATFORM FOR CREATING KNOWLEDGE-BASED SYSTEMS

В статье исследуются проблемы автоматизации и интеллектуальной поддержки процесса математического и имитационного моделирования сложных объектов за счёт комбинации компонентно-ориентированного и онтологического подходов. В качестве основной прикладной области для применения обсуждаемых методов и средств предполагается использовать такое направление, как комплексное моделирование окружающей среды. В контексте изучаемых вопросов рассмотрены современные подходы к автоматизации компонентно-ориентированного моделирования. При интеграции компонентов-моделей в единую результирующую комплексную модель разработчику необходимо не только обеспечить формальное согласование со стандартами используемого каркаса моделирования, но и учитывать различные типы семантической и синтаксической неоднородности компонентов. В связи с этим выполнена классификация типов интеграции комплексных моделей, обсуждаются особенности реализации компонентно-ориентированного моделирования в авторской платформе создания систем, основанных на знаниях. В качестве иллюстративного примера рассматривается гидролого-экологическая балансовая модель. The article considers the problems of automation and intellectual support of the mathematical and simulation modeling process of complex objects via a combination of component-based and ontological approaches. As the main application area for the discussed methods and tools, it is proposed to use the integrated environmental modeling domain. In this context, modern approaches to the automation of component-based modeling are considered. To couple model components into a final complex model, the developer needs not only to ensure formal agreement with the standards of the modeling framework but also to take into account various types of semantic and syntactic heterogeneity of components. In this regard, the classification of the integration types for complex modeling is carried out, the related implementation features in the author's platform for creating knowledge-based systems are discussed. The hydrological-ecological balance model is considered an illustrative example.

Environmental Performance of Wood Bioconcretes with Different Wood Shavings Treatments

The wood bio-concrete (WBC) production is a solution for the advancement of sustainable construction, since it has the potential to recycle waste in the form of shavings generated in wood processing and stock CO2, contributing for climate change reduction. However, the chemical incompatibility between plant biomass and cementitious matrix leads to the need for previous treatment of wood shavings to application in bio-concretes. In the present study, one heat treatment and two alkaline treatments with immersion in Ca (OH)2 solution were evaluated using Life Cycle Assessment (LCA) methodology. The environmental modeling was performed by SimaPro, using the Ecoinvent database, and primary data collected in the laboratory. The potential environmental impacts were related to the compressive strength of produced WBC (in MPa) as an ecoefficiency indicator. Considering the functional unit of mechanical performance, the alkaline treatment with two immersions was the one that generated less environmental impacts.

Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images

Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.

Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.

Development and Evaluation of the Aerosol Forecast Member in NCEP’s Global Ensemble Forecast System (GEFS-Aerosols v1)

NOAA’s National Weather Service (NWS) is on its way to deploy various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in a collaboration between the Global Systems Laboratory, Chemical Science Laboratory, the Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component (NGAC) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in GEFS is based on the Weather Research and Forecasting model (WRF-Chem) that was previously included into FIM-Chem (Zhang et al, 2021). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke was implemented; the GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL); the Blended Global Biomass Burning Emissions Product (GBBEPx V3) provides biomass burning emission and Fire Radiative Power (FRP) data; and the global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid scale transport and deposition is handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by NCEP’s operational Global Forecast System based on FV3 (FV3GFS). This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement, satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGAC system.

Climate Change Impacts on Agriculture in Latin America and the Caribbean: An Application of the Integrated Economic-Environmental Modeling (IEEM) Platform

In this paper, we assess the economy-wide impact of Climate Change (CC) on agriculture and food security in 20 Latin American and the Caribbean (LAC) countries. Specifically, we focus on the following three channels through which CC may affect agricultural and non-agricultural production: (i) agricultural yields; (ii) labor productivity in agriculture, and; (iii) economy-wide labor productivity. We implement the analysis using the Integrated Economic-Environmental Model (IEEM) and databases for 20 LAC available through the OPEN IEEM Platform. Our analysis identifies those countries most affected according to key indicators including Gross Domestic Product (GDP), international commerce, sectoral output, poverty, and emissions. Most countries experience negative impacts on GDP, with the exception of the major soybean producing countries, namely, Brazil, Argentina and Uruguay. We find that CC-induced crop productivity and labor productivity changes affect countries differently. The combined impact, however, indicates that Belize, Nicaragua, Guatemala and Paraguay would fare the worst. Early identification of these hardest hit countries can enable policy makers pre-empting these effects and beginning the design of adaptation strategies early on. In terms of greenhouse gas emissions, only Argentina, Chile and Uruguay would experience small increases in emissions.

Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

Optimizing nature-based solutions by combining social equity, hydro-environmental efficiency, and economic costs through a novel Gini coefficient

A robust multi-functional decision support system for widespread planning of nature-based solutions (NBSs) must incorporate components of social equity. NBS systems advance social well-being through enhanced levels of greenspace, which have been shown to improve physical health (e.g., heart disease, diabetes), mental health (e.g., post-traumatic stress disorder, depression), and socio-economics (e.g., property values, aesthetics, recreation). However, current optimization frameworks for NBSs rely on stormwater quantity abatement and, to a lesser extent, economic costs and environmental pollutant mitigation. Therefore, the objective of this study is to explore how strategic management strategies associated with NBS planning may be improved, while considering the tripartite interactions between hydrological, environmental, and societal conditions. Here, a large-scale NBS watershed was calibrated to local conditions using standard hydro-environmental modeling (i.e., EPA’s SWMM) and optimized on the basis of stormwater abatement, pollutant load reduction, and economic efficiency. The spatial allocation of possible NBS features was integrated with properties of social equity through a novel framework involving the Area Deprivation Index (ADI) and a composite Gini coefficient. By embedding social equity into the fabric of the NBS planning process, we provide an opportunity for improving social justice and spurring further community buy-in toward a balanced system. This study demonstrates how the optimal spatial placement of NBSs is location-dependent according to both the physical and human properties of the watershed.