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Search Results (18)
  • Open Access

    ARTICLE

    Formation of Water Quality of Surface Water Bodies Used in the Material Processing

    Tatyana Lyubimova1,*, Anatoly Lepikhin2, Yanina Parshakova1, Irina Zayakina3, Alibek Issakhov4

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 815-828, 2024, DOI:10.32604/fdmp.2024.048463 - 28 March 2024

    Abstract In the process of production or processing of materials by various methods, there is a need for a large volume of water of the required quality. Today in many regions of the world, there is an acute problem of providing industry with water of a required quality. Its solution is an urgent and difficult task. The water quality of surface water bodies is formed by a combination of a large number of both natural and anthropogenic factors, and is often significantly heterogeneous not only in the water area, but also in depth. As a rule,… More >

  • Open Access

    ARTICLE

    Inversion of Water Quality TN-TP Values Based on Hyperspectral Features and Model Validation

    Yaping Luo1, Na Guo1,*, Dong Liu2, Shuming Peng3, Xinchen Wang4, Jie Wu3

    Revue Internationale de Géomatique, Vol.32, pp. 39-52, 2023, DOI:10.32604/RIG.2023.046014 - 20 December 2023

    Abstract Using hyperspectral data collected in January and June 2022 from the Sha River, the concentrations of total nitrogen (TN) and total phosphorus (TP) were estimated using the differential method. The results indicate that the optimal bands for estimation vary monthly due to temperature fluctuations. In the TN model, the power function model at 586 nm in January exhibited the strongest fit, yielding a fit coefficient (R2) of 0.95 and F-value of 164.57 at a significance level (p) of less than 0.01. Conversely, the exponential model at 477 nm in June provided the best fit, with R2 = More > Graphic Abstract

    Inversion of Water Quality TN-TP Values Based on Hyperspectral Features and Model Validation

  • Open Access

    ARTICLE

    Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

    Wenwu Tan1, Jianjun Zhang1,*, Xing Liu1, Jiang Wu1, Yifu Sheng1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    Journal on Big Data, Vol.5, pp. 85-97, 2023, DOI:10.32604/jbd.2022.030908 - 26 December 2023

    Abstract At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of More >

  • Open Access

    ARTICLE

    Suitability and Sustainability of Rainwater Quality Monitoring System in Cistern for Domestic Use

    Kenedy A. Greyson*

    Journal on Internet of Things, Vol.5, pp. 1-11, 2023, DOI:10.32604/jiot.2023.040255 - 14 September 2023

    Abstract Rainwater harvesting (RWH) systems have been the source of domestic water for many years and still becoming essential in many communities of developing countries. However, due to various reasons, there are several sources of contamination in the rainwater cistern systems. Dissolved chemicals from the roofing, storage, and conveyance materials, together with the suspended particulate matter from the airborne, are examples of water contamination. In this work, the water quality monitoring system has been designed and implemented. Chemical and physical parameters of water samples were collected from three locations using a data acquisition (DAQ) system and More >

  • Open Access

    ARTICLE

    A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

    Wen Yee Wong1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045 - 30 August 2023

    Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers… More >

  • Open Access

    ARTICLE

    Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

    Azimbek Khudoyberdiev1, Mohammed Abdul Jaleel1, Israr Ullah2, DoHyeun Kim3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5471-5499, 2023, DOI:10.32604/cmc.2023.036898 - 29 April 2023

    Abstract The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations. Internet of Things (IoT) based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production. This objective requires intensive monitoring, prediction, and control by optimizing leading factors that impact fish growth, including temperature, the potential of hydrogen (pH), water level, and feeding rate. This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming. The proposed fish farm control… More >

  • Open Access

    ARTICLE

    Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

    Po-Yuan Yang1,*, Yu-Cheng Liao2, Fu-I Chou2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2861-2880, 2023, DOI:10.32604/csse.2023.036810 - 03 April 2023

    Abstract Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated… More >

  • Open Access

    ARTICLE

    Chicken Eggshell as an Innovative Bioflocculant in Harvesting Biofloc for Aquaculture Wastewater Treatment

    Hajjar Hartini Wan Jusoh1, Nor Azman Kasan2,*, Hidayah Manan2, Nurfarahana Mohd Nasir1,3, Fareza Hanis Mohd Yunos1, Sofiah Hamzah1, Ahmad Jusoh1,2,*

    Journal of Renewable Materials, Vol.11, No.5, pp. 2321-2332, 2023, DOI:10.32604/jrm.2023.026086 - 13 February 2023

    Abstract Implementation of biofloc technology (BFT) system in aquaculture industry shows high productivity, low feed conversion ratio, and an optimum culture environment. This study was divided into two phases. The first phase involved maintaining the water quality using the optimum carbon-to-nitrogen ratio by manipulating pH in culture water. The second phase examined the performance of harvesting biofloc (remaining phytoplankton and suspended solids in the system) using chicken eggshell powder (CESP). This study showed that pH 7 to 8 were the best biofloc performance with high removal percentage of ammonia (>99%) with a remaining ammonia concentration of… More > Graphic Abstract

    Chicken Eggshell as an Innovative Bioflocculant in Harvesting Biofloc for Aquaculture Wastewater Treatment

  • Open Access

    ARTICLE

    Application of Time Serial Model in Water Quality Predicting

    Jiang Wu1, Jianjun Zhang1, Wenwu Tan1, Hao Lan1,*, Sirao Zhang1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 67-82, 2023, DOI:10.32604/cmc.2023.030703 - 22 September 2022

    Abstract Water resources are an indispensable and valuable resource for human survival and development. Water quality predicting plays an important role in the protection and development of water resources. It is difficult to predict water quality due to its random and trend changes. Therefore, a method of predicting water quality which combines Auto Regressive Integrated Moving Average (ARIMA) and clustering model was proposed in this paper. By taking the water quality monitoring data of a certain river basin as a sample, the water quality Total Phosphorus (TP) index was selected as the prediction object. Firstly, the… More >

  • Open Access

    ARTICLE

    Design of a Web Crawler for Water Quality Monitoring Data and Data Visualization

    Ziwen Yu1, Jianjun Zhang1,*, Wenwu Tan1, Ziyi Xiong1, Peilun Li1, Liangqing Meng2, Haijun Lin1, Guang Sun3, Peng Guo4

    Journal on Big Data, Vol.4, No.2, pp. 135-143, 2022, DOI:10.32604/jbd.2022.031024 - 31 October 2022

    Abstract Many countries are paying more and more attention to the protection of water resources at present, and how to protect water resources has received extensive attention from society. Water quality monitoring is the key work to water resources protection. How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection. In this paper, python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality Database (GEMStat) sites, and the multi-thread parallelism was added to More >

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