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 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 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 Analisis Intervensi Kebijakan Kenaikan Tarif Dasar Listrik (TDL) Tahun 2017 Terhadap Pemakaian Listrik Wilayah SULSELRABAR

2020 ◽  
Vol 2 (1) ◽  
pp. 31
Author(s):  
Ahmad Zaki ◽  
Rahmat Syam ◽  
Ahmad Firjatullah Hakim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Step Function ◽  
Government Policies ◽  
Series Data ◽  
Intervention Analysis ◽  
Intervention Model ◽  
Diagnosis Model ◽  
The Impact ◽  
Time Of Intervention

Penelitian ini merupakan penelitian terapan mengenai analisis intervensi yang memodelkan data time series yang dipengaruhi oleh adanya suatu kejadian atau intervensi Penelitian ini bertujuan untuk menentukan model intervensi fungsi step dengan waktu intervensi T (mei 2017) yang didapatkan dari proses pemodelan ARIMA preintervensi, identifikasi responintervensi, estimasi parameter intervensi dan pemeriksaan diagnosis model intervensi. Adapun data yang digunakan adalah data pemakaian listrik (dalamKWh), kategori rumah tangga dengan daya 900 VA, wilayah Sulawesi Selatan Tenggara Barat (SULSELRABAR) periode Januari 2016 sampai dengan Desember 2017 yang diperoleh dari PT. PLN Persero Wilayah SULSELRABAR Makassar. Berdasarkan hasil analisis didapatkan bahwa terjadi penurunan terhadap pemakaian listrik pada bulan setelah terjadinya intervensi sebagai dampak dari kebijakan pemerintah yang menaikkan tarif dasar listrik (didefinisikan sebagai intervensi).Kata kunci: Analisis intervensi, fungsi step, ARIMA, time series This research is an implementation research about intervention analysis that modelling time series data effected by the existence of an event or intervention. This research aimed to determine the model of intervention of  the step function with time of intervention (T) derived from process of ARIMA preintervensi modelling, identification of response of intervention, intervention parameter estimation and examination diagnosis of intervention model. As for the data that was used in the form of data of the using of electricity (in KWh), the category of households with power of  900 VA, South Southeast West Sulawesi Region  (SULSELRABAR) from January, 2016 to December, 2017 were obtained from PT PLN Persero SULSELRABAR Area Of Makassar. Based on the analysis result obtained that there is derivation towards the using of electricity in the month after the intervention, it shows the impact of government policies that raising the electricity base tarif rate (defined as the intervention).Keywords: Intervention Analysis, Step Function, ARIMA, Time Series.

Impact of the As Samra wastewater treatment plant upgrade on the water quality (COD, electrical conductivity, TP, TN) of the Zarqa River

2013 ◽  
Vol 67 (7) ◽  
pp. 1455-1464 ◽  
Author(s):  
A. Al-Omari ◽  
Z. Al-houri ◽  
R. Al-Weshah
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Time Series ◽  
Wastewater Treatment ◽  
Time Series Data ◽  
Wastewater Reuse ◽  
Treatment Plant ◽  
Series Data ◽  
Before And After ◽  
Zarqa River

The impact of the As Samra wastewater treatment plant upgrade on the quality of the Zarqa River (ZR) water was investigated. Time series data that extend from October 2005 until December 2009 obtained by a state-of-the-art telemetric monitoring system were analyzed at two monitoring stations located 4 to 5 km downstream of the As Samra effluent confluence with the Zarqa River and about 25 km further downstream. Time series data that represent the ZR water quality before and after the As Samra upgrade were analyzed for chemical oxygen demand (COD), electrical conductivity (EC), total phosphorus (TP) and total nitrogen (TN). The means of the monitored parameters, before and after the As Samra upgrade, showed that the reductions in the COD, TP and TN were statistically significant, while no reduction in the EC was observed. Comparing the selected parameters with the Jordanian standards for reclaimed wastewater reuse in irrigation and with the Ayers & Westcot guidelines for interpretation of water quality for irrigation showed that the ZR water has improved towards meeting the required standards and guidelines for treated wastewater reuse in irrigation.

scholarly journals Predicting the Trend of Dissolved Oxygen based on kPCA-RNN Model

2019 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter Fitch ◽  
Peter J. Thorburn
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Data Driven ◽  
Quality Data ◽  
Series Data ◽  
Water Quality Data ◽  
Sensory Data ◽  
Water Quality Variables

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

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