A comparison of statistical methods for evaluating missing data of monitoring wells in the Kazeroun Plain, Fars Province, Iran

Asgharinia, Shahla; Petroselli, Andrea

doi:10.1016/j.gsd.2019.100294

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…The model specification adopted in this study did not allow for any missing values in the variables. Hence, any missing values for the variables were imputed by interpolation from the nearest stations [ 61 ]. In this study, kriging was used to interpolate the value of a variable at unsampled locations based on the measurement at nearby locations by fitting a semivariogram model which is a function of spatial distance.…”

Section: Methodsmentioning

confidence: 99%

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

et al. 2021

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Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

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Section: Methodsmentioning

confidence: 99%

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

et al. 2021

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“…Besides this, it has been used to classify GWL hydrographs [8], to study the changes in GWL produced after an earthquake in Japan [9] and to evaluate subsurface flow patterns [10]. Recently clustering was also used to study hydrochemical impacts on groundwater by altering the interaction between groundwater and surface water [11] and to evaluate the missing data of monitoring wells [12].…”

Section: Introductionmentioning

confidence: 99%

Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain

Naranjo‐Fernández

Guardiola‐Albert

Aguilera

et al. 2020

Water

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Groundwater resources are regularly the principal water supply in semiarid and arid climate areas. However, groundwater levels (GWL) in semiarid aquifers are suffering a general decrease because of anthropic exploitation of aquifers and the repercussions of climate change. Effective groundwater management strategies require a deep characterization of GWL fluctuations, in order to identify individual behaviors and triggering factors. In September 2019, the Guadalquivir River Basin Authority (CHG) declared that there was over-exploitation in three of the five groundwater bodies of the Almonte-Marismas aquifer, Southwest Spain. For that reason, it is critical to understand GWL dynamics in this aquifer before the new Spanish Water Resources Management Plans (2021–2027) are developed. The application of GWL series clustering in hydrogeology has grown over the past few years, as it is an extraordinary tool that promptly provides a GWL classification; each group can be related to different responses of a complex aquifer under any external change. In this work, GWL time series from 160 piezometers were analyzed for the period 1975 to 2016 and, after data pre-processing, 24 piezometers were selected for clustering with k-means (static) and time series (dynamic) clustering techniques. Six and seven groups (k) were chosen to apply k-means. Six characterized types of hydrodynamic behaviors were obtained with time series clustering (TSC). Number of clusters were related to diverse affections of water exploitation depending on soil uses and hydrogeological spatial distribution parameters. TSC enabled us to distinguish local areas with high hydrodynamic disturbance and to highlight a quantitative drop of GWL during the studied period.

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“…Often the groundwater level data sets compiled at the regional scales are patchy and spatially unevenly distributed (Barthel et al, 2021). Groundwater hydrographs rarely have equal observation periods and frequencies, and missing values are ubiquitous (Asgharinia and Petroselli, 2020;Peterson et al, 2017). The commonly required observation regularity is daily or weekly data (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, this risks creating biases by introducing too many similar values (Pratama et al, 2016). Nevertheless, summary statistic imputation is routinely used to treat large databases because of its simplicity (e.g., Asgharinia and Petroselli (2020)). Even more common is linear interpolation for infilling relatively short gaps in groundwater level time series.…”

Section: Introductionmentioning

confidence: 99%

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Bikše¹,

Retiķe²,

Haaf³

et al. 2023

Preprint

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Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The value of the developed gap introduction approach lies in aiding the selection of imputation methods, a task not limited to groundwater level time series alone. The results further provide insights into region-specific data peculiarities that can assist groundwater analysis and modelling.

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A comparison of statistical methods for evaluating missing data of monitoring wells in the Kazeroun Plain, Fars Province, Iran

Cited by 14 publications

References 23 publications

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

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