Time Series Of Monitoring Data Research Articles

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.

Early neurological deterioration detection with a transformer convolutional auto-encoder model

This paper proposes an improved transformer convolutional auto-encoder model combined with the exponentially weighted moving average (EWMA) control chart to detect early neurological deterioration (END) of ischemic stroke patients after endovascular therapy in advance. In the proposed method, the transformer convolutional auto-encoder is used to extract crucial features of multivariate clinical monitoring time series data and obtain the reconfiguration loss while EWMA control chart is utilized to monitor the derived reconfiguration loss and identify anomalies. To verify the feasibility and effectiveness of the proposed END detection approach, multivariate clinical monitoring time series data of ischemic stroke patients in the neurocritical care unit from Beijing Tiantan hospital are collected. Meanwhile, the proposed approach is benchmarked with seven state-of-the-art models. The computation results show that the proposed approach achieves the best performance with the lowest false alarm rate and the highest detection rate. Therefore, the proposed END detection model is practical to guide doctors in conducting clinical interventions in advance to prevent deterioration in patients with ischemic stroke.

Field study of energy and environmental performance of ground source heat pumps retrofitted in a cluster of UK social housing dwellings

This paper uses an empirical field based approach to examine seasonal variation in heat pump electricity use and indoor temperatures across a cluster of 50 social housing dwellings retrofitted with ground source heat pumps (GSHP). The case study dwellings were co-located in a socially deprived housing estate in Oxford (UK). Time series monitoring data was collected on indoor, outdoor and target temperatures, heat pump electricity use (kWh), space heating and hot water operation status (on/off) in non-heating and heating seasons. Contextual data about the dwellings were gathered through heat pump surveys and energy performance certificates. Results showed 60% rise in electricity use during the heating season with statistically significant correlation observed between indoor and target temperatures, energy efficiency rating and monthly electricity use. When electricity use was normalised for outdoor temperature, lower electricity use per heating degree day (HDD) was observed in the heating season in the majority of dwellings, confirming the efficient running of the heat pumps. Mean daily indoor temperatures were found to be stable across majority of dwellings, with peak electricity use largely happening outside of the national evening peak period of 4pm-7pm. Despite there being a ‘Timer’ mode in relation to outside weather, most of the residents used the ‘Manual’ mode of operation to adjust target temperatures, possibly to overcome the low output temperatures of heat pumps. While resident training will help to enhance understanding of heating controls, smarter operation of heat pumps through automation based on optimisation of indoor and outdoor temperature, occupancy patterns, heating preferences and electricity price signals could result in further energy savings.

Disk failure prediction based on association analysis and SSA-LSTM

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method.

A weakly supervised pairwise comparison learning approach for bearing health quantitative evaluation and remaining useful life prediction

PurposeThe purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.Design/methodology/approachBased on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.FindingsThe method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.Originality/valueThe method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The Impact on Karst Aquifer Regimes Induced by a Surface Reservoir in Karst through Multiparametric Analyses (Reservoir Bileća—Herzegovina)

The results of long-term groundwater monitoring in the Bileća reservoir adjacent (eastern Herzegovina, Bosnia and Herzegovina) indicate that there is a good hydraulic connection between the reservoir and the karst aquifer located in the right bank of the reservoir. In remote parts of the right-bank karst aquifer, due to complex lithostratigraphic characteristics and a tectonic structure, the influence of the reservoir is very variable and has been insufficiently studied up until now. This paper presents the analysis and synthesis of new, relevant hydrogeological data, as a basis for the creation of a 3D spatial model, i.e., the analysis of the karst system’s behaviour, both under regular and artificial hydrological conditions of surface reservoir construction and its continuous use (filling and emptying). The interactive connection of the model and complex surface and groundwater time series monitoring data of this system is presented through the analysis of its hydrodynamic characteristics, which completes the spatial model of the karst regime caused by low and high groundwater levels and low and high reservoir water levels. The integration of all the data into one model and further multiparametric analyses revealed the different types of hydrodynamic conditions prevailing in the karst aquifer with regard to the influence of the high or low waters of the reservoir.

A modified back analysis method for deep excavation with multi-objective optimization procedure

Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety. This paper proposes a modified back analysis method with multi-objective optimization procedure, which enables a real-time prediction of horizontal displacement of retaining pile during construction. As opposed to the traditional stage-by-stage back analysis, time series monitoring data till the current excavation stage are utilized to form a multi-objective function. Then, the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification. The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages. To achieve efficient parameter optimization and real-time prediction of system behavior, the back propagation neural network (BPNN) is established to substitute the finite element model, which is further implemented together with MOPSO for automatic operation. The proposed approach is applied in the Taihu tunnel excavation project, where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data. The method is reliable with a prediction accuracy of more than 90%. Moreover, different optimization algorithms, including non-dominated sorting genetic algorithm (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO, are compared, and their influences on the prediction accuracy at different excavation stages are studied. The results show that MOPSO has the best performance for high dimensional optimization task.

Evaluation of residential demand response trials with smart heat pumps and batteries and their effect at the substation feeder

Residential demand response using low carbon technologies can potentially offer energy flexibility to the electricity network along with integration of renewable generation. This paper seeks to empirically evaluate the effectiveness of residential demand response trials on a low voltage feeder of a secondary substation in Barnsley (UK). The study used a sample of 14 well-insulated dwellings equipped with home batteries, heat pumps and solar photovoltaic systems coordinated using automated control. Statistical analysis was undertaken using time-series monitoring data obtained at the individual dwelling level, dwelling sample and at the feeder level of the local low voltage network. Resident experience of the trials was assessed through qualitative data obtained from household telephone surveys. Over three weeks of trials, daily demand response interventions of 2 h duration were applied to the sample of 14 dwellings. For evening peak times, the mean reduction in grid electricity import was found to be 1.3 kWh (67%) per dwelling for turn-down interventions which aimed to minimise import. For turn-up interventions between 1 and 3 pm, the mean increase in grid electricity import was found to be 5.8 kWh (645%) per dwelling. The effect of interventions was measured at the low voltage network level for two single-phase feeders, where penetration of trial homes was approximately one-third. A reduction in mean real power up to 21% was observed for turn-down interventions as well as an increase in real mean power up to 307% for turn-up interventions. In general, the trials had little effect on residents in terms of thermal comfort, hot water availability, noise disturbance or disruption to routines, and where such effects were noticed, they were broadly acceptable. The widespread implementation of residential demand response schemes will require increased roll-out of time-of-use tariffs, enhanced resident support and extensive monitoring of low voltage feeders in electricity substations.

Response of runoff to climate change in the area of runoff yield in upstream Shiyang River Basin, Northwest China: A case study of the Xiying River

The objective of this study was to analyze the response of runoff in the area of runoff yield of the upstream Shiyang River basin to climate change and to promote sustainable development of regional water resources and ecological environment. As the biggest tributary of the Shiyang River, Xiying River is the only hydrological station (Jiutiaoling) that has provincial natural river and can achieve long time series monitoring data in the basin. The data obtained from this station is representative of natural conditions because it has little human activites. This study built a regression model through identifying the characteristics of runoff and climate change by using Mann-Kendall nonparametric statistical test, cumulative anomaly, and correlation analysis. The results show that the average annual runoff is 320.6 million m³/a with the coefficient of variation of 0.18 and shows slightly decrease during 1956–2020. It has a significant positive correlation the average annual precipitation (P<0.01). Runoff is sensitive to climate change, and the climate has becoming warm and wet and annual runoff has entering wet period from 2003. Compared to the earlier period (1955–2000), the increases of average annual temperature, precipitation and runoff in recent two decades were 15%, 9.3%, and 7.8%, respectively. Runoff in the Shiyang River is affected by temperature and precipitation among climate factors, and the simulation results of the runoff-climate response model (R = 0.0052P − 0.1589T + 2.373) indicate that higher temperature leads to a weakening of the ecological regulation of surface runoff in the flow-producing area.

Identification and Analysis of Unstable Slope and Seasonal Frozen Soil Area along the Litang Section of the Sichuan–Tibet Railway, China

The Sichuan–Tibet Railway (STR) is currently under construction and serves as an important transportation route in western China. Identifying potential geohazards along the route is important for project construction. However, research on the frozen soil of the Western Sichuan Plateau, and on frozen soil identification using interferometric synthetic aperture radar (InSAR) is relatively negligible. As a low-cost, all-weather spatial geodesy tool, InSAR is frequently used for geohazard identification. We selected a study area located along the Litang section of the STR, starting from Litang County in the east and extending 60 km to the west. The geological conditions along the line are complex, with numerous fault zones and hidden danger points for landslide. To identify unstable slopes along the line, distribute scatterer InSAR (DS-InSAR) was used to obtain surface displacement information from 2018 to 2021. Based on the displacement information obtained from the ascending and descending orbit images from Sentinel-1, a spatial density clustering method identified 377 and 388 unstable slopes in the study area, respectively, of these, 132 were consistent. The identified unstable slopes were mostly located in areas with a relatively high altitude and moderate slope. The Luanshibao landslide, which is a typical landslide in the study area, had notable signs of displacement, where the displacement rate along the back edge of the landslide can reach 20 mm/a. An inversion method for the seasonal frozen soil area distribution was proposed based on the periodic subsidence and uplift model and time-series monitoring data; the calculated seasonal freeze–thaw amplitude exceeded 20 mm. Further analysis revealed a 2-month lag in the response of the freeze–thaw phenomenon to the air temperature. This study demonstrated that DS-InSAR offers optimal surface displacement data, which can provide an important basis to identify engineering geological hazards.

A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data

Mine slope landslides seriously threaten the safety of people’s lives and property in mining areas. Landslide prediction is an effective way to reduce losses due to such disasters. In recent years, micro-deformation monitoring radar has been widely used in mine slope landslide monitoring. However, traditional landslide prediction methods are not able to make full use of the diversified monitoring data from these radars. This paper proposes a landslide time prediction method based on the time series monitoring data of micro-deformation monitoring radar. Specifically, deformation displacement, coherence and deformation volume, and the parametric degree of deformation (DOD) are calculated and combined with the use of the tangent angle method. Finally, the effectiveness of the method is verified by using measured data of a landslide in a mining area. The experimental results show that our proposed method can be used to identify the characteristics of an imminent sliding slope and landslide in advance, providing monitoring personnel with more reliable landslide prediction results.

Long-term monitoring in transition: Resolving spatial mismatch and integrating multistate occupancy data

The success of long-term wildlife monitoring programs can be influenced by many factors and study designs often represent compromises between spatial scales and costs. Adaptive monitoring programs can iteratively manage this tension by adopting new cost-efficient technologies, which can provide projects the opportunity to reallocate costs to address new hypotheses, adapt to changing ecological conditions, or adjust sampling scale or resolution. If there is interest in longer time series of monitoring data, methodological transitions may necessitate integrated models to link newer data with historical data. However, data integration can be difficult if spatial or temporal scales are mismatched. Here, we develop an integrated multistate site-occupancy model and resolve sample unit spatial mismatch to link datasets from two northern spotted owl (Strix occidentalis caurina) monitoring schemes that broadly overlapped during a methodological transition. The first dataset was obtained from a decades-long spotted owl monitoring program using call-playback and mark-resight surveys on historical territories of varying size and shape. This monitoring program has recently transitioned to passive acoustic monitoring of randomly selected 5-km2 hexagons over larger spatial extents. Both monitoring datasets overlapped with areas in which barred owl (Strix varia), an invasive competitor that has played an important role in northern spotted owl declines, were being removed experimentally. Reconciling spatial mismatch substantially increased the representation of the call-playback dataset and integrating the two datasets increased precision of spotted owl use and paired occupancy estimates relative to single dataset estimates. Estimates of spotted owl pair occupancy across the study area were lower than previous territory-based estimates based on call-playback surveys. Our integrated model further showed that a concurrent barred owl removal experiment increased landscape use and site occupancy by pairs of spotted owls. Our empirical application of an integrated modelling approach demonstrates a useful analytical framework for long-term monitoring efforts undergoing methodological transitions (e.g. mark-recapture to non-invasive population monitoring). This framework allows monitoring programs to maintain continuity of monitoring objectives across methodological transitions, rigorously incorporate previous findings, and adaptively respond to changing ecological conditions.

Application of an improved naive Bayesian analysis for the identification of air leaks in boreholes in coal mines

Borehole extraction is the basic method used for control of gases in coal mines. The quality of borehole sealing determines the effectiveness of gas extraction, and many influential factors result in different types of borehole leaks. To accurately identify the types of leaks from boreholes, characteristic parameters, such as gas concentration, flow rate and negative pressure, were selected, and new indexes were established to identify leaks. A model based on an improved naive Bayes framework was constructed for the first time in this study, and it was applied to analyse and identify boreholes in the 229 working face of the Xiashijie Coal Mine. Eight features related to single hole sealing sections were taken as parameters, and 144 training samples from 18 groups of real-time monitoring time series data and 96 test samples from 12 groups were selected to verify the accuracy and speed of the model. The results showed that the model eliminated strong correlations between the original characteristic parameters, and it successfully identified the leakage conditions and categories of 12 boreholes. The identification rate of the new model was 98.9%, and its response time was 0.0020 s. Compared with the single naive Bayes algorithm model, the identification rate was 31.8% better, and performance was 55% faster. The model developed in this study fills a gap in the use of algorithms to identify types of leaks in boreholes, provides a theoretical basis and accurate guidance for the evaluation of the quality of the sealing of boreholes and borehole repairs, and supports the improved use of boreholes to extract gases from coal mines.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data.

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Probabilistic analysis to correlate seismic data with lava extrusion phases at Merapi volcano (Indonesia)

Volcanoes can produce a range of eruptive behavior even during a single eruption, changing quickly from effusive to explosive style, and the other way around. The changes in eruption phases (e.g. phreatic explosion, magmatic explosion, lava extrusion, etc.) can lead to different volcanic hazards and require timely assessment for the implementation of mitigation measures. Here we explore how to correlate a given eruption phase with changes in the monitoring data using statistical analysis and conditional probabilities. We calculate the success of detection of an eruption phase using a threshold of monitoring data, which includes the uncertainty on the eruption phase dates with a Monte Carlo simulation. We apply the method to dome forming eruptions of Mt. Merapi (Indonesia) and evaluate their time occurrence using an exceptionally long monitoring time series (from 1993 to 2012, over nineteen years) of Multiphase (Hybrid) Seismic Energy. We identify the seismic energy threshold that is associated with the lava extrusion phase with an accuracy of 90 ±2%, precision of 73 ± 2%, specificity of 96 ± 1%, and sensitivity of 56 ± 1%. We further test our method with the recent 2018 eruption (not used in the thresholds calculations) and we identify the lava extrusion with a precision of 67%, specificity of 70%, and sensitivity of 92%. We also seismically detected the 2018′s onset of the lava extrusion phase 14 days earlier than the visual observation. Given the link between dome-collapse pyroclastic flows and growth episodes of the lava dome at Merapi, our analysis also allows us to establish that 83% of the most energetic pyroclastic flows occur within the first 3 months after the onset of lava extrusion phase. Our method can be applicable to a range of time series of monitoring data (seismic, deformation, gas) and to other volcanoes that have a significant number of past events.

Different storm responses of organic carbon transported to Lake Taihu by the eutrophic Tiaoxi River, China

Low-frequency high-magnitude storms can flush disproportionate amounts of terrigenous dissolved organic carbon (DOC) and particulate organic carbon (POC) into rivers during a short period. However, previous studies focused on the impacts of storms on organic carbon transport in headwater streams that are minimally influenced by human activities and are far from lakes. To better estimate the lake carbon budget and manage lake water environments, we need to understand the transport of storm-induced organic carbon into lakes by eutrophic rivers. Based on daily and hourly time-series monitoring data, this paper systematically studied the influences of storm precipitation on DOC and POC transport in the eutrophic Tiaoxi River entering Lake Taihu, the 3rd largest freshwater lake in China. The results showed that seven storms transported 59% of the annual total organic carbon into Lake Taihu in 2019, and all storms resulted in transport peaks. During the storm period on August 9–16, 2019, DOC was negatively related to the water level (r = −0.44, p < 0.05), but POC responded positively (r = 0.52, p < 0.05); allochthonous organic carbon contents were elevated, but the autochthonous components were diluted. Moreover, the storm-induced input of riverine organic carbon influenced the lake water environment across a large region, and the impacts lasted more than 10 days. These findings have important implications for accurately estimating riverine organic carbon fluxes into lakes and making better-informed decisions about when to pump drinking water from lakes.

Spatio-temporal variability of Brachyura larval assemblages in mangroves of the Gulf of Guayaquil’s inner estuary

Mangroves are the dominant coastal vegetation in tropical estuaries and host a large diversity of brachyuran crabs. Crab larvae have adapted to dynamic hydrological conditions in estuaries using several physiological and behavioral strategies. Changes in crab larval assemblages can be monitored in time in order to detect ecological changes occurring within estuaries. For this study, we sampled quarterly crab larval assemblages in two sub-estuaries (Salado and Churute) of the Gulf of Guayaquil (GG) during 2016–2017. The largest community dissimilarities occurred between sub-estuaries, with Salado having higher morphological diversity and larval densities than Churute. Temporal patterns were only evident within each sub-estuary, and in relation to total and individual morpho-species density values. Ocypodidae larvae were dominant across samples; with zoea I of the red mangrove crab (Ucides occidentalis) showing maximal density values during spring tides. Hydrological parameters (i.e., turbidity, conductivity, salinity and temperature) contributed to only 19% of the variability in biota, demonstrating that crab larvae are physiologically adapted to a wide range of water conditions. This study established an ecological baseline for future studies using crab larval assemblages in the assessment of ecological changes occurring in mangrove tidal creeks of the GG. However, longer time-series monitoring data and description of new species and stages of crab larvae are necessary to better assess potential future changes.

Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements

This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.

A Filtering Algorithm for Marine Environmental Time Series Monitoring Data Based on Kinect Depth Information

A Filtering Algorithm for Marine Environmental Time Series Monitoring Data Based on Kinect Depth Information

Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes due to Precipitation Events Using Time Series Monitoring Data

Significant variation in the precipitation events caused by global climate change has made it difficult to manage water resources due to the increased frequency of unexpected droughts and floods. Under these conditions, groundwater is needed to ensure a sustainable water supply; thus, estimates of precipitation recharge are essential. In this study, we derived an apparent recharge coefficient (ARC) from a modified water table fluctuation equation to predict groundwater storage changes due to precipitation events. The ARC is calculated as the ratio of the recharge rate over the specific yield (R/Sy); therefore, it implicitly expresses variation in Sy. The ARC varies spatially and temporally, corresponding to the precipitation events and hydrogeological characteristics of unsaturated materials. ARCs for five monitoring wells from two basins in Korea in different seasons were calculated using a 10-year groundwater level and weather dataset for 2005–2014. Then, the reliability of the ARCs was tested by the comparison of the predicted groundwater level changes for 2015 and 2016 with observed data. The root mean square error ranged from 0.03 to 0.09 m, indicating that the predictions were acceptable, except for one well, which had thick clay layers atop the soil layer; the low permeability of the clay slowed the precipitation recharge, interfering with groundwater level responses. We performed a back-calculation of R from the Sy values of the study areas; the results were similar to those obtained via other methods, confirming the practical applicability of the ARC. In conclusion, the ARC is a viable method for predicting groundwater storage changes for regions where long-term monitoring data are available, and subsequently will facilitate advanced decision making for allocating and developing water resources for residents, industry, and groundwater-dependent ecosystems.