scholarly journals Pola dan distribusi temperatur dan TDS di tiga lokasi sungai segmen Cimahi-Bandung Utara dan perkiraan sumbernya

2018 ◽  
Author(s):  
Sri Aditya ◽  
Dasapta Erwin Irawan
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Open Source ◽  
River Water ◽  
Quality Data ◽  
Series Data ◽  
Random Pattern ◽  
Water Quality Data ◽  
River Bank ◽  
Current Form

Complete file thesis is available on Overleaf platform https://www.overleaf.com/read/wqqfskwyhjyk data while all data and R codes are available on Github https://github.com/dasaptaerwin/Pola-dan-distribusi-temperatur-dan-TDS-air-sungai-di-Bandung << Bahasa Indonesia >> Variasi harian data kualitas air dapat dianalisis untuk mengetahui proses yang terjadi pada air sungai itu sendiri juga interaksinya dengan air tanah, khusus pada zona hyporheic. Observasi dilakukan di tiga lokasi anak Sungai Cikapundung di tahun 2017 (periode Maret-November 2017). Pengukuran dilakukan pada tiga lokasi di DAS S. Cikapundung (diurutkan dari utara-selatan): S. Ciawitali lokasi Curug Panganten (CP) dan Grand Royal Pancanaka (GRP), S. Cibeureum lokasi Pondok Hijau Indah (PHI). Tata guna lahan berevolusi dari lahan terbuka berupa hutan dan lahan perkebunan/pertanian di lokasi CP dan GRP, ke perumahan di PHI. Sungai di ketiga lokasi itu menjadi muara dari saluran-saluran air yang melewati kawasan di tepi kiri dan kanannya.Pengukuran debit (meter/detik), temperatur air sungai (derajat Celcius), temperatur udara (derajat Celcius), dan TDS (total dissolved solids) (ppm). Pengukuran dilakukan dengan alat portabel merk Lutron, masing-masing dengan ketelitian 0.01 pada masing-masing satuan yang berkaitan. Pengukuran dilakukan empat kali di masing-masing lokasi: pukul 10.00, 12.00, 14.00, dan 16.00. Data kemudian dianalisis menggunakan piranti lunak open source R untuk teknik time series.Hasil pengukuran di ketiga lokasi tersebut menunjukkan variasi mingguan dan bulanan. Untuk variasi minggu, nilai TDS naik mulai hari Jumat dan turun pada hari Senin. Lokasi yang paling konsisten menunjukkan gejala ini adalah PHI. Variasi bulanan menunjukkan peningkatan di bulan Juni dan turun di bulan Juli. Pola ini terjadi di tiga lokasi tersebut. Pada titik ini, kami berpendapat bahwa pola tersebut diduga berkaitan dengan aktivitas manusia yang meningkat di akhir minggu. Untuk pola bulanan, ada indikasi bahwa peningkatan TDS bersamaan dengan liburan Lebaran 2017. Dugaan tersebut perlu diklarifikasi lebih lanjut dengan pengukuran kandungan nutrien (nitrat, nitrit, fosfat, klorin, dan sulfat) secara time series. Dari riset ini, dapat kami sampaikan bahwa data time series sangat berperan dalam analisis lingkungan, sehingga layak untuk dikembangkan. <<<<<<<<<<<<<<<<<<<<<<< In English >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Many processes can influence river water and groundwater to its current form. Daily variation of water quality data can be analyzed to understand such processes. This research mainly analyzed time series data of TDS from river water to under- stand the processes. We suspect that local drainage has a strong influence to the increasing values of TDS in the river water. We collected the data from March to November 2017 at three locations (from north to south): S. Ciawitali located at Curug Panganten (CP) and at Grand Royal Pancanaka (GRP), and S. Cibeureum located at Pondok Hijau Indah (PHI). At each locations we measured air temper- ature, river water temperature, and TDS. The measurements were conducted four times/day, 3 days/week in eight months. To support our claims, we also analyzed 310 water quality dataset that were available to classify the samples. We used open source applications, R, to produce the calculation and the plots. From the three locations, we find that TDs values on CP and PHI show a cyclic weekly pattern, with the values from PHI are averagely 20% higher than values from CP at given period. We don’t find the same pattern at GRP. The values from that location show a random pattern. Interestingly, we find an increasing trend from June to July. We argue that the cyclic pattern at CP and PHI are brought by many drainage outlets in the river bank. Such drainage collects domestic waste from housings and nearby accommodations (hotels) and tourist objects. Both locations are known as part of tourist object area at northern Bandung. GRP does not show the same situation because the TDS most likely only from the nearby GRP housing. The observation site is located at a man made channel that connect two natural channel through GRP housing complex. We argue that the TDS values at the channel capture a closed system drainage, compare to the open system at CP and PHI. Based on the multi- variable analysis, we also see a close interaction between groundwater and river water at various places in Bandung area. This phenomenon should add our under- standing on the patterns of TDS value. Such close interactions between groundwater and river water, should be the focus of the Bandung authorities. In this such close interactions, the contamination present in the river environment could come both from the river and the groundwater system. Both water have the same chance to send out man-made pollution in the environment.

scholarly journals Time series modeling of biochemical oxygen demand at the upstream catchment of Feitsui Reservoir, Taiwan

2016 ◽  
Vol 47 (5) ◽  
pp. 1069-1085 ◽  
Author(s):  
Yung-Chia Chiu ◽  
Chih-Wei Chiang ◽  
Tsung-Yu Lee
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Biochemical Oxygen Demand ◽  
Oxygen Demand ◽  
Quality Data ◽  
Series Data ◽  
Water Quality Data ◽  
Inference System ◽  
Feitsui Reservoir ◽  
Upstream Catchment

The adaptive neuro fuzzy inference system (ANFIS) has been proposed to model the time series of water quality data in this study. The biochemical oxygen demand data collected at the upstream catchment of Feitsui Reservoir in Taiwan for more than 20 years are selected as the target water quality variable. The classical statistical technique of the Box-Jenkins method is applied for the selection of appropriate input variables and data pre-processing of using differencing is implemented during the model development. The time series data obtained by ANFIS models are compared to those obtained by autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs). The results show that the ANFIS model identified at each sampling station is superior to the respective ARIMA and ANN models. The R values at all sampling stations of the training and testing datasets are 0.83–0.98 and 0.81–0.89, respectively, except at Huang-ju-pi-liao station. ANFIS models can provide accurate predictions for complex hydrological processes, and can be extended to other areas to improve the understanding of river pollution trends. The procedure of input selection and the pre-processing of input data proposed in this study can stimulate the usage of ANFIS in other related studies.

scholarly journals GRQA: Global River Water Quality Archive

2021 ◽  
Vol 13 (12) ◽  
pp. 5483-5507
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
River Water ◽  
Environmental Sustainability ◽  
Large Scale ◽  
Observation Time ◽  
River Water Quality ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data

Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 (Virro et al., 2021).

Applications of intervention analysis to model the impact of drought and bushfires of Water Quality

1989 ◽  
Vol 40 (3) ◽  
pp. 241 ◽  
Author(s):  
DR Welsh ◽  
DB Stewart
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Time Series ◽  
Time Series Data ◽  
Statistical Modelling ◽  
Quality Data ◽  
Series Data ◽  
Intervention Analysis ◽  
Water Quality Data ◽  
The Impact

Intervention analysis is a rigorous statistical modelling technique used to measure the effect of a shift in the mean level of a time series, caused by an intervention. A general formulation of an intervention model is applied to water-quality data for two streams in north-eastern Victoria, measuring the effect of drought on the electrical conductivity of one stream, and the effect of bushfires on the flow and turbidity of the other. The nature of the intervention is revealed using exploratory data-analysis techniques, such as smoothing and boxplots, on the time-series data. Intervention analysis is then used to confirm the identified changes and estimate their magnitude. The increased level of electrical conductivity due to drought is determined by three techniques of estimation and the results compared. The best of these techniques is then used to model changes in stream flow and turbidity following bushfires in the catchment.

scholarly journals Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling

2018 ◽  
Vol 22 (2) ◽  
pp. 1175-1192 ◽  
Author(s):  
Qian Zhang ◽  
Ciaran J. Harman ◽  
James W. Kirchner
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Irregular Sampling ◽  
Quality Data ◽  
Sampled Data ◽  
Spectral Slope ◽  
Water Quality Data ◽  
Fractal Scaling ◽  
Wide Range ◽  
Irregularly Sampled Data

Abstract. River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling. Traditional methods for estimating fractal scaling – in the form of spectral slope (β) or other equivalent scaling parameters (e.g., Hurst exponent) – are generally inapplicable to irregularly sampled data. Here we consider two types of estimation approaches for irregularly sampled data and evaluate their performance using synthetic time series. These time series were generated such that (1) they exhibit a wide range of prescribed fractal scaling behaviors, ranging from white noise (β  =  0) to Brown noise (β  =  2) and (2) their sampling gap intervals mimic the sampling irregularity (as quantified by both the skewness and mean of gap-interval lengths) in real water-quality data. The results suggest that none of the existing methods fully account for the effects of sampling irregularity on β estimation. First, the results illustrate the danger of using interpolation for gap filling when examining autocorrelation, as the interpolation methods consistently underestimate or overestimate β under a wide range of prescribed β values and gap distributions. Second, the widely used Lomb–Scargle spectral method also consistently underestimates β. A previously published modified form, using only the lowest 5 % of the frequencies for spectral slope estimation, has very poor precision, although the overall bias is small. Third, a recent wavelet-based method, coupled with an aliasing filter, generally has the smallest bias and root-mean-squared error among all methods for a wide range of prescribed β values and gap distributions. The aliasing method, however, does not itself account for sampling irregularity, and this introduces some bias in the result. Nonetheless, the wavelet method is recommended for estimating β in irregular time series until improved methods are developed. Finally, all methods' performances depend strongly on the sampling irregularity, highlighting that the accuracy and precision of each method are data specific. Accurately quantifying the strength of fractal scaling in irregular water-quality time series remains an unresolved challenge for the hydrologic community and for other disciplines that must grapple with irregular sampling.

scholarly journals Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2411
Author(s):  
Seulbi Lee ◽  
Jaehoon Kim ◽  
Jongyeon Hwang ◽  
EunJi Lee ◽  
Kyoung-Jin Lee ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Euclidean Algorithm ◽  
Quality Monitoring ◽  
Quality Data ◽  
Time Warping ◽  
Water Quality Data ◽  
Dynamic Time

It is essential to monitor water quality for river water management because river water is used for various purposes and is directly related to the health and safety of a population. Proper network installation and removal is an important part of water quality monitoring and network operation efficiency. To do this, cluster analysis based on calculated similarity between measuring stations can be used. In this study, we measured the similarities between 12 water quality monitoring stations of the Bukhan River. River water quality data always have a station-dependent time lag because water flows from upstream to downstream; therefore, we proposed a Dynamic Time Warping (DTW) algorithm that searches for the minimum distance by changing and comparing time-points, rather than using the Euclidean algorithm, which compares the same time-point. Both Euclidean and DTW algorithms were applied to nine water quality variables to identify similarities between stations, and K-medoids cluster analysis were performed based on the similarity. The Clustering Validation Index (CVI) was used to select the optimal number of clusters. Our results show that the Euclidean algorithm formed clusters by mixing mainstream and tributary stations; the mainstream stations were largely divided into three different clusters. In contrast, the DTW algorithm formed clear clusters by reflecting the characteristics of water quality and watershed. Furthermore, because the Euclidean algorithm requires the lengths of the time series to be the same, data loss was inevitable. As a result, even where clusters were the same as those obtained by DTW, the characteristics of the water quality variables in the cluster differed. The DTW analysis in this study provides useful information for understanding the similarity or difference in water parameter values between different locations. Thus, the number and location of required monitoring stations can be adjusted to improve the efficiency of field monitoring network management.

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