One of the essential computations in ocean environmental study is semantic computing for interpreting and analyzing the changes of various situations (coral area, ocean water, places of ocean livings, sea level, etc.). It is important to realize global ocean-environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way from a viewpoint of global environments. It is also significant to memorize those situations and compute ocean-environment change in various aspects and contexts, in order to discover what are happening in the nature of ocean. We have various aspects and contexts in ocean-environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for finding actual aspects and contexts existing in oceans. We have proposed a new method for Differential Computing in our Multi-dimensional World map in [3,4]. We utilize a multi-dimensional semantic computing model, and a multi-dimensional space with an adaptive-axes selection mechanism to implement semantic computing [1,2]. Computing ocean-environmental changes in multi-aspects and contexts using semantic computing, important factors that change natural ocean-environment are highlighted. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new concept of “Coral-Health-Level Analysis Semantic-Space for Ocean-environment” for realizing global Ocean-environmental analysis. This space and computing method are based on environmental-database creation with coral-health-level-analysis sensors for analyzing and interpreting environmental phenomena and changes occurring in the oceans. This paper focuses on coral-health-level in South-East Asian Ocean area, as an experimental study for creating “Coral-Health-Level Analysis Semantic-Space for Ocean-environment”. We have created a semantic-space for coral-health-level analysis in the South-East Asian Ocean area with 24-dimensional axes (coral-health-level parameters). As the first step, this space is applied to South-East Asian Ocean area, and it is expandable to multiple spots in Ocean-areas to analyze and compare their coral-health-level situations in the global scope for Ocean-environmental analysis. In this paper, we also apply 5-Dimensional World Map System for semantic computing to analysis in South-East Asian Ocean area, as an international collaborative platform for Ocean-environment analysis with spatio-temporal and semantic dimensions.

Global warming and climate change affect not only all living things but also many non-living things. Furthermore, those phenomena caused extreme disasters that become impossible to ignore. Coral bleaching is a phenomenon to show ocean warming due to climate change. This paper presents the analysis and visualization of the coral health levels database by using 5D World Map System. Coral health levels are analyzed using a coral-knowledge image that includes coral with a coral health chart. We use image processing and color semantic distance to interpret coral health levels. We have implemented an actual space integration system to access environmental information resources with coral health levels and image analysis that the results have been shown on the 5D World Map System. As for the experiment study, the study areas of coral health levels analysis are located in the ocean close to Thailand’s islands as Ko Ha (Five Island), Ko Bon, Ko Hin Ngam, Ko Tarutao, Ko Thalu, and Ko Samaesarn.

Semantic computing is essentially significant for realizing the semantic interpretation of natural and social phenomena and analyzes the changes of various environmental situations. The 5D World Map (5DWM) System [4,6,8] has introduced the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for global environmental system integrations [1–4], as a global environmental knowledge sharing, analysis and integration system. Environmental knowledge base creation with 5D World Map is realized for sharing, analyzing and visualizing various information resources to the map which can facilitate global phenomena-observations and knowledge discoveries with multi-dimensional axis control mechanisms. The 5DWM is globally utilized as a Global Environmental Semantic Computing System, in SDGs 9, 11, 14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes) for observing and analyzing disaster, natural phenomena, ocean-water situations with local and global multimedia data resources. This paper proposes a new semantic computing method as an important approach to semantic analysis for various environmental phenomena and changes in a real world. This method realizes “Self-Contained-Knowledge-Base-Image” & “Contextual-Semantic-Interpretation” as a new concept of “Coral-Health-level Analysis in Semantic-Space for Ocean-environment” for global ocean-environmental analysis [8,9,12,18]. This computing method is applied to automatic database creation with coral-health-level analysis sensors for interpreting environmental phenomena and changes occurring in the oceans in the world. We have focused on an experimental study for creating “Coral-Health-level Analysis Semantic-Space for Ocean-environment” [8,9,12,18]. This method realizes new semantic interpretation for coral health-level with “coral-images and coral-health-level knowledge-chart”.

This paper presents a comparison of prediction methods for a water quality index (WQI) that is used for classification of water quality in rivers or canals. In this work, we consider the water quality index of two canals namely Phadung Krung Kasem Canal and Saen Saep Canal, Bangkok, Thailand as a case study. We compare results from M5P, M5Rules, REPTree with results from multilayer perceptron. The models employ five input variables including dissolved oxygen (DO), biological oxygen demand (BOD), ammonia nitrogen (NH3-N), Fecal Coliform bacteria (FCB) and Total Coliform bacteria (TCB) which were measured in the canals. The data in this research had been collected from Bangkok Metropolitan Authority, Thailand from 1 January 2007 to 31 November 2017. The total number of data is 2,000 records. The 10-fold cross validation method is used for evaluation of prediction models. It allows to determine the most effective method. Our experimental results show that the REPTree method yielded the highest accuracy to predict water quality index compared to other methods proposed in this paper.

The significant computation in global environmental analysis is "context-oriented semantic computing" to interpret the meanings of natural phenomena occurring in the nature. Our semantic computing method realizes the semantic interpretation of natural phenomena and analyzes the changes of various environmental situations. It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in global environments. Semantic computations contribute to make "appropriate and urgent solutions" to the changes of environmental situations. It is also significant to memorize those situations and compute environment changes in various aspects and contexts, in order to discover what are happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes, and it is essential to realize a new analyzer for computing the meanings of those situations and making solutions for discovering actual aspects and contexts. We propose a new method for semantic computing in our Multi-dimensional World map. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM) [1–3], and a multi-dimensional space with an adaptive axis adjustment mechanism. In semantic computing for environmental changes in multi-aspects and contexts, we present important functional pillars for analyzing natural environment situations. We also present a method to analyze and visualize the highlighted pillars using our Multi-dimensional World Map (5-Dimensional World Map) System. We introduce the concept of "SPA (Sensing, Processing and Analytical Actuation Functions)" for realizing a global environmental system, to apply it to Multi-dimensional World Map System. This concept is essential to design environmental systems with Physical-Cyber integration to detect environmental phenomena in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This system currently realizes the integration and semantic-analysis for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with image databases. We have implemented an actual space integration system for accessing environmental information resources and image analysis.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their modelling. In this paper, we discuss on different aspects of conceptual modelling in global environmental context. The paper is the summary of the panel session “The Future of Conceptual Modelling” in the 29th International Conference on Information Modelling and Knowledge Bases.

The amount of chlorophyll in a plant can indicate leaf N concentration, as chlorophyll is a major component of nitrogen. Identifying chlorophyll levels in plants, therefore, leads to appropriate nitrogen fertilizer recommendations for the plants and to their fertilization at the proper time, optimizing efficiency of agricultural production and helping to preserve the environment by reducing excess use of nitrogen fertilizer. Measuring leaf N concentration in a high accuracy laboratory is expensive and time consuming, whereas a SPAD chlorophyll handheld meter can be carried conveniently in the field and can assess rapidly. However, the handheld meter's capacity is limited, especially in large areas, where samples must be taken to represent an area. The use of vegetation indices calculated from UAV imagery and correlated with SPAD values enables better estimations of N leaf concentrations over large areas. The result showed that relationship between NDVI and SPAD value (chlorophyll contents) of waxy corn (Zea mays L. var. ceratina) at V6 and R1 stage in R2were 0.594 and 0.632, respectively. The results of this study are beneficial for knowing the amount of chlorophyll in plants, which can result in accurate nitrogen fertilizer requirements and better administration of management zones in precision agriculture.

Semantic computing integration with deep-learning realizes a new artificial brain-memory system. We have presented a concept of “MMM: Semantic Computing System” for analyzing and interpreting environmental phenomena and changes occurring in the oceans and rivers in the world. We also introduce the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This paper presents integration and semantic-analysis methods for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with Coral-Image Analysis in two environmental-semantic spaces with water-quality and image databases. We have implemented an actual space integration system for accessing environmental information resources with water-quality and image analysis. We clarify the feasibility and effectiveness of our method and system by showing several experimental results for environmental medical document data Environmental-semantic space integration realizes deep analysis environmental phenomena and situations. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (air, water, CO2, places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. In the design of environment-analysis systems, one of the most important issues is how to integrate several environmental aspects and analyze environmental data resources with semantic interpretations. In this paper, we present an environmental-semantic computing system. Our environmental-semantic computing system realizes integration and semantic-search among environmental-semantic spaces with waterquality and image databases.

This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.

This paper presents a global sharing analysis and visualization of water quality analysis by 5D World Map (5DWM) system. The data resources in this research collected from Sichang Island, Chonburi province, Thailand during 1990 to 2002 and 2010 to 2017. Six parameters of water quality were selected during 1990 to 2002 i.e. chlorophyll a, ammonia, nitrite, nitrate, phosphate and. The total locations sites were 21 stations which situated around Sichang Island. Otherwise, ten parameters were selected during 2010 to 2016 i.e. temperature, salinity, dissolved oxygen (DO), pH, ammonia, nitrite, nitrate, phosphate, silicate, and alkalinity. On the other hand, eight parameters were selected in February and July 2017 i.e. temperature, pH, oxidation reduction potential (ORP), conductivity, turbidity, dissolved oxygen (DO), TDS (Total Dissolved Solids), and salinity. All parameter of water quality was added and display by 5DWorld Map in order to visualize and sharing the water quality. Our results showed that 5DWorld Map can apply to environmental analysis and semantic computing. We apply the dynamic evaluation and mapping functions of multiple views of temporal-spatial metrics, and integrate the results of semantic evaluation to analyze environmental multimedia information resources. 5D World Map System for world-wide viewing for Global Environmental Analysis for water quality around Sichang Island, Thailand was reported in this study.

This research deals with the monitoring techniques using normal and multi-spectrum cameras to detect coral healthiness. The Acropora Corals were investigated in laboratory experiment. Parameter of investigation is Ammonia contamination from 0.01 ppm to 10.0 ppm. Under these conditions, the images of coral were captured and analyzed. An Entropy and coral indices of the obtained images were proposed. The important finding in this study is the feature extraction of coral healthiness: (1) event detection, (2) % degradation and (3) relationship between coral index and entropy. For event detection, the trends of water quality parameters such as temperature and salinity was considered. The obtained entropy values gave % degradation of coral. It was also found that GRCI index related with the relevant entropy values of coral images.

Evapotranspiration (ET) is the sum of evaporation and transpiration from the surface to the atmosphere. ET is a hydrologic cycle component and importance in agricultural water management such as schedules of irrigation water requirement, irrigation system design and watershed management. Evapotranspiration can be measured or estimated using several methods. The objectives of this study were simple methods and quick analyses for the estimating of crop coefficients (Kc) and daily actual evapotranspiration (ETa) from Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and Sentinel-2 satellite images. Subsequent comparison of ETafrom both satellite images at Ban Nong Bua, Amphoe Ban Fang, Khon Kaen Province, Thailand. The results showed that good correlation between NDVI and ETafrom both satellite images (coefficient of determination (R2) = 0.85 and 0.87, respectively). Both satellite images can be used or combined to detect temporal and spatial analysis. Sentinel-2 can provide more fine resolution of 10 m in VIS-NIR bands than Landsat 8 and is suitable for precision farming or site specific management, while Landsat 8 TIRS bands were advantages.

This research was to investigate the amount of methane emission in the organic paddy field (wet-season rice), direct-seeding method around Tung Kula Rong Hai, the Northeast of Thailand during planting and harvest season and to examine the influential variables such as the growth, types of soil, temperature, height of rice plant, density of rice plant and the water level in the paddy field. The study was conducted in the paddy field of Khao Dawk Mali 105 in 4 stations. The results of the study revealed that; in period 1, from the period of seeding to tillering, CH4Flux is in the range of 0.193–1.383 mg CH4m−2hr−1or 0.728 ± 0.429 mg CH4m−2hr−1in average; in period 2, from tillering to ripening, CH4Flux is 1.311–2.446 mg CH4m−2hr−1with the average of 1.938 ± 0.359 mg CH4m−2hr−1; In period 3, from ripening to harvesting, CH4Flux of 0.873–2.235 mg CH4m−2hr−1with the average of 1.575 ± 0.351 mg CH4m−2hr−1

Environmental-semantic space integration is a promising approach to realize deep analysis environmental phenomena and situations. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (air, water, CO2, places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. In the design of environment-analysis systems, one of the most important issues is how to integrate several environmental aspects and analyze environmental data resources with semantic interpretations. In this paper, we present an environmental-semantic computing system. Our environmental-semantic computing system realizes integration and semantic-search among environmental-semantic spaces with water-quality and image databases. We have already presented a concept of “Semantic Computing System” for analyzing and interpreting environmental phenomena and changes occurring in the oceans and rivers in the world. We also introduce the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This paper presents integration and semantic-analysis methods for two environmental-semantic spaces with water-quality and image databases. We have implemented an actual space integration system for accessing environmental information resources with water-quality and image analysis. We clarify the feasibility and effectiveness of our method and system by showing several experimental results for environmental medical document databases.

This paper presents an alum dosage prediction in coagulation process by using Weka Data Mining Software. The data in this research had been collected from Dongmarkkaiy Water Treatment Plant (DWTP), Vientiane capital, Laos PDR from 1st January 2008 to 31st October 2016. The total number of collected data were 2,891 records. In this research, we compared the results from multilayer perceptron (MLP), M5Rules, M5P, and REPTree method by using the root mean square error (RMSE) and mean absolute error (MAE) value. Three input independent variables, i.e. turbidity, pH, and alkalinity were used. The dependent variable was alum added for the coagulation process. Our experimental results indicated that the MLP method yielded the highest precision method in order to predict the alum dosage.

Three species of corals at Sichang Island i.e. Acropora sp. Goniopora sp. and Pavona sp. were subjected to a stress test with low salinity and normal salinity at concentration 10, 20 and 30 psu, respectively. Under water photograph and eye observation of coral activity were recorded at 12, 24 and 48 hours. The entropy or surface roughness and percent polyp activity were analyzed with comparison to eye observation of coral activity. The experiment was carried out under continuously water temperature and underwater light intensity controlled. The results indicated that “Healthy” entropy values for Acropora sp. are 1.57–1.62 and for Goniopora sp. are 4.26–4.46. In contrast, for Pavona sp., short polyp coral, there was no “Healthy” entropy value resulted from any photographic assessment in this study. The “Healthy” value of Acropora sp. evaluated from percent active polyp was more than 52.4. For conclusion, The Entropy and percent active polyp values were suitable tools for Acropora sp. and Pavona sp., a short polyp coral's healthy identification. Whereas the eye observation of coral activity both percent polyp extend and percent polyp long was suitable for Goniopora sp., a long polyp coral.

Artificial neural network is a common method which has been used in many works. This paper presents a comparison of prediction methods for alum dosage using in water supply treatment process. In this research, we compared results from M5P, M5Rules and REPTree to the results from multilayer perceptron, one type of artificial neural network. Six input variables, i.e. turbidity, alkalinity, pH, conductivity, color and suspended solids relating to reaction of coagulation were used. The data in this research had been collected from Bangkhen Branch Office of Metropolitan Waterworks Authority, Thailand from 1 January 2006 – 31 July 2015. Our experimental results showed that the M5Rules method yielded the highest accuracy to predict Alum dosage comparing to other methods run in this study. For M5P and M5Rules, building model by using smoothing procedure and unpruned technique appears to give out the best model to predict with the highest accuracy.

Dengue fever is a communicable disease that attacks more than 120 countries in the world during 50 years. Therefore, it is to make sense to say that collaboration among the countries, especially neighborhood countries, is one important key to combat the dengue. Currently, except a serological collaboration, collaboration in dengue is sporadic and temporal. This paper addresses the initiative to build vector-control strategy collaborative among Surabaya (Indonesia), Kuala Lumpur (Malaysia), and Bangkok (Thailand). Deriving the global policy from World Health Organization (WHO), we build the system that (1) extracting global feature from the local feature, (2) selecting the significant features, to determine ranking of importance of a feature, by weighting a feature, and (3) matching the pattern of data to the suitable strategy by measuring the similarity. We built the system from the real data of the Surabaya, Kuala Lumpur and Bangkok in 2012. We verified reliability of the system by comparing the data with the actual action in January 2012 The result shows that the system is system feasible to be implemented, however we still need more preparation to implement the system.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. In this environmental study, we will use context-dependent differential computation to analyse changes in various factors (temperatures, colours, level of CO2, habitats, sea levels, coral areas, etc.). In this paper, we will discuss a global environmental computing methodology for analysing the diversity of nature and animals, using a large amount of information on global environments.

This paper presents the analysis and visualization of river-water quality in 25 rivers in Thailand by using 5D World Map system. Water pollution is analyzed by using Water Quality Index (WQI) and Metal Index (MI) which focus on Ping, Nan and Chao Phraya River (the important rivers of Thai). The WQI indicator was used to evaluate water quality by conductivity, NO3-N, NO2-N, NH3-N, Cd, Cr, Mn, Ni, Pb, Zn and As. The MI indicator was used to estimate concentration of metal in the river. The results on 5D World Map System show that several actual values assigned to water-quality parameters are shown in snapshots. The results of Water Quality Index (WQI) show the WQI levels 32.697 at Chaophraya river (Bangkok, 2004) and 38.534 at Ping river (Nakhonsawan, 2014) for Irrigation and Aquatic life respectively, and can be classified into categories of quality-levels for Irrigation and Aquatic life. The results of Metal Index (MI) show that the MI level reaches 92.902 at Ping River (Nakhonsawan, 2014) and 1803.303 at Ping River (Nakhonsawan, 2014) for Irrigation and Aquatic life respectively.