scholarly journals Kualitas Fisika-Kimia Air di Areal Budidaya Desa Kaima, Eris dan Toulimembet, Kabupaten Minahasa, Provinsi Sulawesi Utara

2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Zainal M. DeBreving ◽  
Robert J. Rompas
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Quality Criteria ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
In Situ Observation ◽  
Water Quality Criteria ◽  
Activity Data ◽  
Farming Area

ABSTRACT Water physical-chemical quality at fish farming area in Kaima, Eris and Toulimembet villages around Lake Tondano was observed. Water quality parameters including temperature, pH, brightness and dissolved oxygen were measured in situ. Observation was conducted at three stations, which were waters around Kaima, Eris, and Toulimembet villages. Measurement was carried out at 2-points at each station, where appointing of both points was based on the consideration of the activities of fish farming and settlement layout. Point-1 represented the activities of fish farming whereas point-2 represented the settlement activity. Data were collected at one week interval. Results showed that water temperature ranged from 26 - 27 0C; brightness was above 2 m; dissolved oxygen ranged from 2.5 - 8.3 ppm and pH ranged from 7,9 - 8,7. Based on the water quality criteria for aquaculture, water quality parameters on fish farming area in Kaima, Eris and Toulimembet, were still suitable for fish farming activities. Keywords: Lake Tondano, water quality, fish farming

scholarly journals Potensi budidaya ikan di Waduk Embung Klamalu Kabupaten Sorong Provinsi Papua Barat: Kajian kualitas fisika kimia air

2013 ◽  
Vol 1 (3) ◽  
Author(s):  
Agustina Frasawi ◽  
Robert J Rompas ◽  
Juliaan Ch. Watung
Keyword(s):  
Carbon Dioxide ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Good Condition ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Physical Chemical ◽  
Papua Barat

The objective of this research was to measure and analyze the water quality parameters including temperature, brightness, pH, dissolved oxygen, total alkalinity, carbon dioxide and BOD in reservoir Embung Klamalu Sorong regency, and to know the factors that affected the water quality of Embung Klamalu. Measurement of water quality parameters was done in situ for temperature, brightness, pH and in laboratory for dissolved oxygen, total alkalinity, carbon dioxide, and BOD. The results showed the temperature at the five observation stations ranged from 26.2 to 29.8 0C, brightness 38 to 46 cm, pH 7.20 to 8.48 mg /L, dissolved oxygen from 7.20 to 8.48 mg / L, alkalinity 100 to 150 mg /L, carbon dioxide from 25.90 to 28.95 mg / L, BOD from 0.20 to 0.38. Refers to the standards of water quality according to the PP. 82, 2001, it could be concluded that water physical-chemical qualities in fish farming locations in the Village Klamalu were still in good condition. Keywords: Water physical-chemical quality, aquaculture, waduk Embung Klamalu

scholarly journals ANALISIS KUALITAS PERAIRAN DI SUNGAI KOMERING DESA ULAK JERMUN KABUPATEN OGAN KOMERING ILIR SEBAGAI DASAR PENGELOLAAN BUDIDAYA IKAN SISTEM KERAMBA

2018 ◽  
Vol 5 (2) ◽  
pp. 182-194
Author(s):  
Reno Irawan ◽  
Robiyanto Hendro Susanto ◽  
Mohamad Rasyid Ridho
Keyword(s):  
Water Quality ◽  
River Water ◽  
Quality Criteria ◽  
Quality Standard ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Survey Method ◽  
Water Quality Criteria ◽  
River Waters

ABSTRACT                The increasing of people amount, the more activities done happen around the Komering River. Many activities done can potentially lead to a decrease in water quality in the river. This study aimed to analyze the water quality in accordance with the raw river water quality criteria for class I and to analyze the water quality state in the Komering river of  Ulak Jermun village Sirah Pulau Padang District. This research was conducted in the Komering Ulak Jermun village Sirah Pulau Padang District from November to December 2016. The observation of water samples conducted at the Laboratory Pengujian Terpadu of Chemistry Faculty of Mathematics University of Sriwijaya. This research used survey method that consists of three stations with 9 sampling points and sampling was conducted 4 times in a month. Based on the analysis of water quality parameters are still within the range of quality standards among others, temperature, pH, BOD5 and phosphate while the water quality parameters exceed the quality standard that TSS, dissolved oxygen, COD and ammonia and based on the analysis storet Komering river waters classified into water quality class C, i.e. moderately contaminated. Keywords: Komering River, Water Quality, Storet  Methods, Water Quality Index 

scholarly journals Cladoceran abundance in relation to water quality parameters of Chakki Talab, Bodhan, Telangana

2018 ◽  
Vol 8 (01) ◽  
Author(s):  
Vasudha Lingampally ◽  
V.R. Solanki ◽  
D. L. Anuradha ◽  
Sabita Raja
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Oxygen Demand ◽  
Total Dissolved Solids ◽  
Quality Parameters ◽  
Biological Oxygen Demand ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
One Year

In the present study an attempt has been made to evaluate water quality and related density of Cladocerans for a period of one year, October 2015 to September 2016. Water quality parameters such as temperature, PH, total dissolved solids, dissolved oxygen, biological oxygen demand, total alkalinity, total hardness, chlorides, phosphates, and nitrates are presented here to relate with the abundance of Cladocerans. The Cladoceran abundance reflects the eutrophic nature of the Chakki talab.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

scholarly journals Can Decomposition Approaches Always Enhance Soft Computing Models? Predicting the Dissolved Oxygen Concentration in the St. Johns River, Florida

2019 ◽  
Vol 9 (12) ◽  
pp. 2534 ◽  
Author(s):  
Mohammad Zounemat-Kermani ◽  
Youngmin Seo ◽  
Sungwon Kim ◽  
Mohammad Ali Ghorbani ◽  
Saeed Samadianfard ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Soft Computing ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Performance Criteria ◽  
Input Combination ◽  
Do Concentration ◽  
Computing Models

This study evaluates standalone and hybrid soft computing models for predicting dissolved oxygen (DO) concentration by utilizing different water quality parameters. In the first stage, two standalone soft computing models, including multilayer perceptron (MLP) neural network and cascade correlation neural network (CCNN), were proposed for estimating the DO concentration in the St. Johns River, Florida, USA. The DO concentration and water quality parameters (e.g., chloride (Cl), nitrogen oxides (NOx), total dissolved solid (TDS), potential of hydrogen (pH), and water temperature (WT)) were used for developing the standalone models by defining six combinations of input parameters. Results were evaluated using five performance criteria metrics. Overall results revealed that the CCNN model with input combination III (CCNN-III) provided the most accurate predictions of DO concentration values (root mean square error (RMSE) = 1.261 mg/L, Nash-Sutcliffe coefficient (NSE) = 0.736, Willmott’s index of agreement (WI) = 0.919, R2 = 0.801, and mean absolute error (MAE) = 0.989 mg/L) for the standalone model category. In the second stage, two decomposition approaches, including discrete wavelet transform (DWT) and variational mode decomposition (VMD), were employed to improve the accuracy of DO concentration using the MLP and CCNN models with input combination III (e.g., DWT-MLP-III, DWT-CCNN-III, VMD-MLP-III, and VMD-CCNN-III). From the results, the DWT-MLP-III and VMD-MLP-III models provided better accuracy than the standalone models (e.g., MLP-III and CCNN-III). Comparison of the best hybrid soft computing models showed that the VMD-MLP-III model with 4 intrinsic mode functions (IMFs) and 10 quadratic penalty factor (VMD-MLP-III (K = 4 and α = 10)) model yielded slightly better performance than the DWT-MLP-III with Daubechies-6 (D6) and Symmlet-6 (S6) (DWT-MLP-III (D6 and S6)) models. Unfortunately, the DWT-CCNN-III and VMD-CCNN-III models did not improve the performance of the CCNN-III model. It was found that the CCNN-III model cannot be used to apply the hybrid soft computing modeling for prediction of the DO concentration. Graphical comparisons (e.g., Taylor diagram and violin plot) were also utilized to examine the similarity between the observed and predicted DO concentration values. The DWT-MLP-III and VMD-MLP-III models can be an alternative tool for accurate prediction of the DO concentration values.

scholarly journals Economic evaluation of the impacts of water quality on fishery production in the Estuary Complex of Santos, São Vicente and Bertioga cities, in southeastern coast of Brazil

2020 ◽  
Vol 56 (1) ◽  
pp. 99-110
Author(s):  
Victor Carrozza Barcellini ◽  
Ângela Tavares Paes ◽  
Simone Georges El Khouri Miraglia
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Total Phosphorus ◽  
Linear Models ◽  
Quality Parameters ◽  
Sao Paulo ◽  
Water Quality Parameters ◽  
São Paulo ◽  
Fishing Activity ◽  
Monetary Loss

The present study proposes a diagnosis of water quality and fishery production in the Estuarine Complex of Santos, São Vicente, and Bertioga Cities as a requirement for economic valuation of water pollution impacts on fishing production. In the study period (2009–2014), three water quality parameters were identified (dissolved oxygen, total phosphorus, and nitrate), which occurred more frequently in non-conformity with Brazilian water standards, according to reports released by the Environmental Company of São Paulo State (Companhia Ambiental do Estado de São Paulo — CETESB). For data collection of fishery production, data from the monitoring of Institute of Fisheries of Santos City (Instituto de Pesca de Santos) were used, and 15 species were identified with higher occurrence in the study area. The relation between water quality parameters and fishery production was analyzed with mixed linear models, in which significant values for dissolved oxygen parameters, total phosphorus (positive relation), and nitrate (negative relation) were found. Environmental valuation considered only the direct use values (DUV) component of the valuation of fishery production variation in relation to water quality variation. For this purpose, the Marginal Productivity Method (MPM) of the dose-response function was used, which resulted in a range of monetary loss between US$ 24,760,550.22 and US$ 60,635,978.78. The obtained values represent only a portion of the valuation of economic and environmental loss in the fishing activity (part of DUV). Therefore, economic value calculated is conservative, and although it did not reached the total amount corresponding to all the impacts caused by poor water quality, given the limitations of methods and study period, the obtained values represent the minimum environmental monetary loss.

scholarly journals IOT Based Smart Monitoring System for Fish Farming

2019 ◽  
Vol 8 (6S) ◽  
pp. 420-424 ◽  
Keyword(s):  
Water Quality ◽  
Current Method ◽  
Fish Farming ◽  
High Water ◽  
Quality Parameters ◽  
Water Levels ◽  
Water Quality Parameters ◽  
Fish Pond ◽  
Fish Ponds ◽  
Fish Tank

The purpose of the current method is to create a safe and secure that helps the fish pond owners and aquatic planters in producing high quality fish by maintaining normal water levels in the fish tank. The flow of the low or high water in the fish pond will solve the long-term problem of killing fish in a fish tank. Each water quality can affect the health of animals alone. The flow of water on fish ponds discusses how every day should be monitored. This should ensure quality by handling the PH, membrane, temperature, ammonia etc. It is a symbol of good quality water quality standards and poor water quality pools and how it should be upgraded. It is recommended that a prerequisite to increase production by ensuring sustainable fresh quality, and consequently, priority should be given priority. Therefore, water quality parameters maintain balanced positions, culture is the basis for the health and development of living organisms. It is recommended to monitor and evaluate water quality parameters on a regular basis

scholarly journals Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

2011 ◽  
Vol 15 (8) ◽  
pp. 2693-2708 ◽  
Author(s):  
A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
O. Jaafar
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effective Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Input Parameters ◽  
Predicted Values

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

scholarly journals Tingkat dan laju penurunan kualitas air di DAS Brantas Malang Raya

2019 ◽  
Vol 5 (2) ◽  
pp. 185
Author(s):  
Ima Yudha Perwira
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Dissolved Oxygen ◽  
Organic Materials ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Decrease Rate ◽  
Better Than

The decrease level of water quality of Brantas Watershed in Malang Raya was observed in this study. The aim of this study was to observe the decrease level of water quality of Brantas Watershed from Batu to Malang City. This study was carried out in the Brantas Watershed of Malang Raya (8 stations: A, B, C, D, E, F, G, and H) for 18,4 Km. The water quality parameters observed in this study were: CODmn (permanganometry), CODcr (CODmn correlation based analysis), dissolved oxygen (DO) (Winkler iodometry), TDS and electrical conductivity (EC) (EC meter), pH (pH meter), and turbidity (Turbidity meter). The result showed the value of CODmn: 1,8-10,2 mg/L, CODcr: 5,6-31,5 mg/L, DO: 4,0-6,1 mg/L, TDS: 204-289 mg/L, EC: 430-617 µS/cm, pH: 7,1-7,6, and turbidity: 2,02-10,30 NTU. There are 3 stations (A, B, and C) with 1st class water quality, 1 station (D) with the 2nd class water quality, and 4 stations (E, F, G, and H) with 3rd class water quality. The decrease of water quality in the Brantas Watershed from Batu to Malang City was up to 3 times with a decrease rate of 2,3 mg/L-1Km-1. The decomposition of organic materials in the water of Batu City and western part of Malang City is relatively better than that of central parts of Malang City which might be caused by the over capacity of recovery (Self-purification mechanism).

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