scholarly journals TINGKAT KESESUAIAN LINGKUNGAN PERAIRAN HABITAT TERIPANG (ECHINODERMATA : HOLOTHUROIDAE) DI KARIMUNJAWA (Environmental Suitability for Holothuroidea Habitat in Karimunjawa)

2017 ◽  
Vol 12 (2) ◽  
pp. 93
Author(s):  
Bambang o Sulardiono ◽  
Pujiono Wahyu Purnomo ◽  
Haeruddin Haeruddin
Keyword(s):  
Water Quality ◽  
Aquatic Environment ◽  
Sea Cucumber ◽  
Current Data ◽  
Quality Data ◽  
Water Quality Data ◽  
Sea Cucumbers ◽  
Water Quality Variables ◽  
Tidal Data

ABSTRAK Ekosistem terumbu karang Karimunjawa menyediakan habitat yang baik bagi kehidupan dan perkembangbiakan teripang. Di sisi lain,  peningkatan beban limbah organik baik bersumber dari daratan maupun dari lingkungan perairan itu sendiri diduga menyebabkan daya dukung untuk kehidupan teripang menurun. Berdasarkan hal tersebut, bagaimana kondisi lingkungan perairan ditinjau dari kesesuaian lingkungan perairan habitat teripang. Pengukuran data kualitas air diambil pada 5 stasiun pengamatan. Data arus berdasarkan  data pasang surut terendah, yang diperoleh dari pengukuran data pasang surut  stasiun LPWP Jepara periode 2010-2011, pengukuran data variabel kedalaman perairan (m), suhu (°C), salinitas (‰), dan pH secara in situ, serta  pengukuran kandungan oksigen terlarut (mg/l)  secara laboratoris. Analisis data tingkat kesesuaian lingkungan  teripang didasarkan atas beberapa kriteria penting yang harus dipenuhi, yaitu kondisi lingkungan yang sesuai dengan standar kriteria kesesuaian, meliputi kisaran dibawah baku mutu dengan skor (1), kisaran toleransi dengan skor (2), dan kisaran optimal dengan skor 3. Selanjutnya dilakukan pembobotan setiap variabel dalam 3 kelas bobot yang diukur berdasarkan tingkat pengaruh masing-masing variable. Berdasarkan hasil perhitungan total  skor (Y) dari 6 variabel kualitas  perairan.diperoleh jumlah skor tertinggi  54 dan terendah 6, sedangkan berdasarkan nilai interval kelas kesesusian  (I) sebesar 16.  Hasil analisis skor per  kelas adalah (a) 39–54 = Sesuai (S1),  (b)  23–38 = Cukup Sesuai (S2), dan (c) 6–22 = Tidak Sesuai (N). Hasil analisis diperoleh informasi bahwa kondisi lingkungan perairan cukup sesuai bagi kehidupan teripang. Kata kunci: Kesesuaian, habitat, teripang ABSTRACT The Karimunjawa waters reef ecosystem provides a good habitat for the life and breeding of sea cucumbers. On the other hand, the increased burden of organic waste both from the mainland and from the water environment itself is thought to cause the carrying capacity for the life of sea cucumbers declined. Based on this, then how the condition of the aquatic environment in terms of the suitability of the marine environment habitat sea cucumbers.  Measurement of water quality data was taken at 5 observation stations. Current data based on the lowest tidal data, obtained from the measurement of the tidal data of LPWP station Jepara period 2010-2011. Measurement of water depth variable (m), temperature (°C), salinity (‰), and pH in situ, and dissolved oxygen content (mg/l) in laboratory. The data analysis of the suitability level of sea cucumber is based on several important criteria that must be fulfilled, that is environmental condition in accordance with standard of conformity criterion, covering range below standard quality with score (1), tolerance range with score (2), and optimal range with score 3, Then weighted each variable in 3 weight classes measured by the influence level of each variable, Based on the result of total score calculation (Y) from 6 water quality variables. Based on the result of total score (Y) of 6 water quality variables. Obtained by the highest score 54 and lowest 6, whereas based on the value of interval of suitability class (I) of 16. The result of the score analysis per class is (a) 39–54 = Suitable (S1), (b) 23–38 = quite suitable  (S2), and (c) 6–22 = Not Match (N). The result of the analysis obtained information that the condition of the aquatic environment is quite suitable for the life of sea cucumber. Keywords: Conformity, habitat, sea cucumber

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals Watershed-Based Evaluation of Automatic Sensor Data: Water Quality and Hydroclimatic Relationships

2020 ◽  
Vol 12 (1) ◽  
pp. 396 ◽  
Author(s):  
Jacopo Cantoni ◽  
Zahra Kalantari ◽  
Georgia Destouni
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Water Discharge ◽  
Sensor Data ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Lake Water Quality ◽  
Water Quality Data ◽  
Blue Green Algae ◽  
Water Quality Variables

Water is a fundamental resource and, as such, the object of multiple environmental policies requiring systematic monitoring of its quality as a main management component. Automatic sensors, allowing for continuous monitoring of various water quality variables at high temporal resolution, offer new opportunities for enhancement of essential water quality data. This study investigates the potential of sensor-measured data to improve understanding and management of water quality at watershed level. Self-organizing data maps, non-linear canonical correlation analysis, and linear regressions are used to assess the relationships between multiple water quality and hydroclimatic variables for the case study of Lake Mälaren in Sweden, and its total catchment and various watersheds. The results indicate water discharge from dominant watersheds into a lake, and lake water temperature as possible proxies for some key water quality variables in the lake, such as blue-green algae; the latter is, in turn, identified as a potential good proxy for lake concentration of total nitrogen. The relationships between water discharges into the lake and lake water quality dynamics identify the dominant contributing watersheds for different water quality variables. Seasonality also plays an important role in determining some possible proxy relationships and their usefulness for different parts of the year.

scholarly journals Predicting sediment and nutrient concentrations from high-frequency water-quality data

2019 ◽  
Author(s):  
Catherine Leigh ◽  
Sevvandi Kandanaarachchi ◽  
James M. McGree ◽  
Rob J. Hyndman ◽  
Omar Alsibai ◽  
...  
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Quality Reporting ◽  
River Level ◽  
Manual Sampling

AbstractWater-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 297-303
Author(s):  
R. M. G. Maravilla ◽  
J. P. Quinalayo ◽  
A. C. Blanco ◽  
C. G. Candido ◽  
E. V. Gubatanga ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Microalgal Biomass ◽  
Chlorophyll A Concentration ◽  
Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

Advanced monitoring of water systems using in situ measurement stations: data validation and fault detection

2013 ◽  
Vol 68 (5) ◽  
pp. 1022-1030 ◽  
Author(s):  
Janelcy Alferes ◽  
Sovanna Tik ◽  
John Copp ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Treatment Plant ◽  
Principal Component ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Data Quality Assessment ◽  
Multiple Sensor ◽  
Pertinent Information

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant.

scholarly journals The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1124 ◽  
Author(s):  
Zaher Yaseen ◽  
Mohammad Ehteram ◽  
Ahmad Sharafati ◽  
Shamsuddin Shahid ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Bat Algorithm ◽  
Specific Conductance ◽  
Least Square ◽  
Quality Data ◽  
Support Vector ◽  
Water Quality Data ◽  
Water Quality Variables ◽  
Do Concentration

The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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