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.

Delaqua – A Prototype Expert System for Operational Control and Management of Lake Water Quality

1991 ◽  
Vol 24 (6) ◽  
pp. 283-290 ◽  
Author(s):  
Frieder Recknagel ◽  
Erhard Beuschold ◽  
Uwe Petersohn
Keyword(s):  
Water Quality ◽  
Expert System ◽  
Lake Water ◽  
Quality Data ◽  
Raw Water ◽  
Operational Control ◽  
Lake Water Quality ◽  
Water Quality Data ◽  
Lakes And Reservoirs ◽  
Raw Water Quality

The expert system DELAQUA (Deep Expert system LAke water QUAlity) combines AI and simulation methods to support decision making in water quality control of lakes and reservoirs. It contains a knowledge base (PROLOG 2), a data base (dBASE III+) and a simulation system (FORTRAN 77) by which the following decision aids can be made available:derivation of recommendations for operational control of undesired impacts on raw water quality by algal blooms or pathogen germsclassification of raw water quality by means of legal standardsdrawing of analogy conclusions by the use of measured and simulated water quality data of reference waterspredictions of raw water quality under changing control strategies and environmental conditions of lakes and reservoirs. The expert system was implemented on an IBM-PC with MS.DOS operating system.

A modeling approach to simulate impact of climate change in lake water quality: phytoplankton growth rate assessment

1998 ◽  
Vol 37 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Hany Hassan ◽  
Keisuke Hanaki ◽  
Tomonori Matsuo
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Growth Rate ◽  
Dissolved Oxygen ◽  
Lake Water ◽  
Phytoplankton Growth ◽  
Quality Data ◽  
Model Parameters ◽  
Lake Water Quality ◽  
Phytoplankton Growth Rate

Global climate change induced by increased concentrations of greenhouse gases (especially CO2) is expected to include changes in precipitation, wind speed, incoming solar radiation, and air temperature. These major climate variables directly influence water quality in lakes by altering changes in flow and water temperature balance. High concentration of nutrient enrichment and expected variability of climate can lead to periodic phytoplankton blooms and an alteration of the neutral trophic balance. As a result, dissolved oxygen levels, with low concentrations, can fluctuate widely and algal productivity may reach critical levels. In this work, we will present: 1) recent results of GCMs climate scenarios downscaling project that was held at the University of Derby, UK.; 2) current/future comparative results of a new mathematical lake eutrophication model (LEM) in which output of phytoplankton growth rate and dissolved oxygen will be presented for Suwa lake in Japan as a case study. The model parameters were calibrated for the period of 1973–1983 and validated for the period of 1983–1993. Meterologic, hydrologic, and lake water quality data of 1990 were selected for the assessment analysis. Statistical relationships between seven daily meteorological time series and three airflow indices were used as a means for downscaling daily outputs of Hadley Centre Climate Model (HadCM2SUL) to the station sub-grid scale.

scholarly journals Water Quality Index Performance for River Pollution Control Based on Better Ecological Point of View (A Case Study in Code, Winongo, Gadjah Wong Streams)

2019 ◽  
Vol 5 (1) ◽  
pp. 47
Author(s):  
Sri Puji Saraswati ◽  
Mochammad Venly Ardion ◽  
Yul Hendro Widodo ◽  
Suwarno Hadisusanto
Keyword(s):  
Water Quality ◽  
River Water ◽  
Pollution Control ◽  
Water Quality Index ◽  
Quality Index ◽  
Stream Water ◽  
Water Discharge ◽  
Monitoring Data ◽  
Quality Data ◽  
Water Quality Variables

The quality of river water quality monitoring data sometimes can be inaccurate. Evaluation of the effectiveness of water pollution control programs needs good quality data to calculate the Water Quality Index (WQI) with the aim to meet the requirement to protect biodiversity and maintain various water functions. Thirty-five water quality variables from Code, Gadjah Wong, and Winongo rivers were taken as data, conducted by Environmental Agency of Yogyakarta in 2004 – 2015. There were only 19 out of 35 water quality variables having good data after improvement of monitoring data, transformation/standardization and analysis of the significant water quality variables with PCA (Principle Component Analysis) and Factor Analysis (FA). WQIs formula in the three rivers used the same 5 significant variables i.e. EC, DO, COD, NH3N, Total Coliform, and "weighted sum index” as the sub-index aggregation technique, with different sub-index coefficients. Winongo River had the best water quality and Gajah Wong River was the worst. According to the relationship of river water discharge and WQIs index, large discharge during rainy seasons does not always decrease the level of pollution, but it tends to increase the WQIs. More effective ways to improve the stream water quality during dry seasons should further be investigated.

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.

scholarly journals Continuous pH, electrical conductivity and temperature measurement at Plynlimon: towards an integrated, reliable and low maintenance instrument system

1997 ◽  
Vol 1 (3) ◽  
pp. 653-660 ◽  
Author(s):  
P. Hodgson ◽  
J. G. Evans
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Conductivity Measurements ◽  
Water Quality Data ◽  
Antecedent Conditions ◽  
Laboratory Reference ◽  
Reference Measurements ◽  
Low Ionic Strength

Abstract. Water quality data for the Nant Tanilwyth stream, Plynlimon (an acidic upland stream, the waters of which are of such low ionic strength that measurement is difficult), has been recorded using a new integrated field instrument system. The negligible drift of the pH electrode allows the system to operate for extended periods (months) without re-calibration, whilst maintaining a standard deviation of 0.19 pH units between its readings and laboratory reference measurements. Conductivity measurements, although within the sensor manufacturer's specification, did not provide meaningful readings at conductivities below 50 μS cm-1. Using the pH data as a surrogate tracer, a high temporal resolution estimate of the stream dynamics, in terms of the contributions of groundwater and soil-water is presented; the dependence of these relative proportions on instantaneous flow and antecedent conditions is shown. It is concluded that, whilst improvements in instrumentation have been made, greater accuracy is still desirable for some scientific applications and ways forward are described.

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 Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

2021 ◽  
Vol 13 (11) ◽  
pp. 6318
Author(s):  
Rafael Rodríguez ◽  
Marcos Pastorini ◽  
Lorena Etcheverry ◽  
Christian Chreties ◽  
Mónica Fossati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Missing Data ◽  
Surface Water ◽  
Water Resource ◽  
Surface Water Quality ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Variables ◽  
Monitoring Stations

The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.

scholarly journals Modelling Water Quality Improvements in a South Korean Inter-Basin Water Transfer System

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3173
Author(s):  
Hye Won Lee ◽  
Bo-Min Yeom ◽  
Jung Hyun Choi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Constructed Wetlands ◽  
Management Practices ◽  
Water Quality Management ◽  
Quality Data ◽  
Watershed Model ◽  
Selective Withdrawal ◽  
Lake Water Quality ◽  
Water Quality Data

In this study, we investigated the feasibility of using constructed wetlands for non-point source pollution reduction. The effect of constructed wetlands in reducing suspended solids (SS) was analyzed using an integrated modeling system of watershed model (HSPF), reservoir model (CE-QUAL-W2), and stream model (EFDC) to investigate the behavior and accumulation of the pollution sources based on 2017 water quality data. The constructed wetlands significantly reduced the SS concentration by approximately 30%, and the other in-lake management practices (e.g., artificial floating islands and sedimentation basins) contributed an additional decrease of approximately 7%. Selective withdrawal decreased in the average SS concentration in the influents by ~10%; however, the effluents passing through the constructed wetlands showed only a slight difference of 1.9% in the average SS concentration. In order to meet the water quality standards, it was necessary to combine the constructed wetlands, in-lake water quality management, and selective withdrawal practices. Hence, it was determined that the model proposed herein is useful for estimating the quantitative effects of water quality management practices such as constructed wetlands, which provided practical guidelines for the application of further water quality management policies.

scholarly journals Numerical Simulation of Donghu Lake Hydrodynamics and Water Quality Based on Remote Sensing and MIKE 21

2020 ◽  
Vol 9 (2) ◽  
pp. 94 ◽  
Author(s):  
Xiaojuan Li ◽  
Mutao Huang ◽  
Ronghui Wang
Keyword(s):  
Remote Sensing ◽  
Numerical Simulation ◽  
Water Quality ◽  
Lake Water ◽  
Quality Data ◽  
Lake Water Quality ◽  
Water Quality Simulation ◽  
Donghu Lake ◽  
Mean Square Errors

Numerical simulation is an important method used in studying the evolution mechanisms of lake water quality. At the same time, lake water quality inversion technology using the characteristics of spatial optical continuity data from remote sensing satellites is constantly improving. It is, however, a research hotspot to combine the spatial and temporal advantages of both methods, in order to develop accurate simulation and prediction technology for lake water quality. This paper takes Donghu Lake in Wuhan as its research area. The spatial data from remote sensing and water quality monitoring information was used to construct a multi-source nonlinear regression fitting model (genetic algorithm (GA)-back propagation (BP) model) to invert the water quality of the lake. Based on the meteorological and hydrological data, as well as basic water quality data, a hydrodynamic model was established by using the MIKE21 model to simulate the evolution rules of water quality in Donghu Lake. Combining the advantages of the two, the best inversion results were used to provide a data supplement for optimization of the water quality simulation process, improving the accuracy and quality of the simulation. The statistical results were compared with water quality simulation results based on the data measured. The results show that the water quality simulation of chlorophyll a and nitrate nitrogen mean square errors fell to 17% and 24%, from 19% and 31% respectively, after optimization using remote sensing spatial information. The model precision was thus improved, and this is consistent with the actual pollution situation of Donghu Lake.

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