Environmental Earth Sciences最新文献

{"title":"Study on progressive damage and deformation law of coal body around borehole under different moisture states","authors":"Hang Zhang,&nbsp;Tianjun Zhang","doi":"10.1007/s12665-024-11955-x","DOIUrl":"10.1007/s12665-024-11955-x","url":null,"abstract":"<div><p>Water immersion in the gas extraction borehole will reduce the stability of the borehole, lead to borehole deformation and collapse, and reduce the efficiency of gas extraction. In order to study the failure characteristics and deformation law of coal and rock bodies around boreholes with different water content, the digital image observation system of coal and rock deformation and failure was used to carry out the surface deformation observation experiment of coal samples with boreholes in dry, natural and saturated states under uniaxial compression. The time series speckle images of the surface deformation of the samples under different stress states were obtained, and the surface deformation of the samples was qualitatively and quantitatively analyzed. The results show that: (1) As the water content increases, the peak strength and modulus of elasticity of the porous specimens gradually deteriorate and decrease, with a maximum deterioration of 39.53% and 17.39%, respectively, and the peak strain gradually increases, with a maximum increase of 40%. (2) The deformation localization phenomenon of the water-containing samples started earlier than that of the dry sample. The deformation localization zones of the dry samples had a faster displacement opening speed and a smaller displacement dislocation amplitude. (3) From dry to water-saturated conditions, the borehole contracted inward by 64.1% overall in the vertical direction and expanded outward by 87.8% overall in the horizontal direction. The higher the water content, the greater the deformation and flattening of the borehole, and the greater the amount of radial displacement and circumferential displacement. (4) Under the action of water-force coupling, the bonding force between particles is reduced, the internal transformation of the specimen to loose and porous, the tensile stress in the pore-fracture stress concentration area is enhanced, and the pores and microcracks develop and expand rapidly, which weakens the bearing capacity of the coal body.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 23","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrochemical stratigraphic analysis of the filling of the Meirama open pit mine II: parameters and elements","authors":"Ricardo Juncosa,&nbsp;Jorge Delgado,&nbsp;José Luis Cereijo","doi":"10.1007/s12665-024-11972-w","DOIUrl":"10.1007/s12665-024-11972-w","url":null,"abstract":"<div><p>In the first article, entitled <i>“Hydrochemical stratigraphic analysis of the filling of the Meirama open pit mine I: Monitoring and filling</i>” (Juncosa et al. Environ Sci Pollut Res 20(11):7520–7533, 2013), the filling process of the old Meirama mining pit (NW Spain), as well as the methodology used in the sample collection and analysis, was described. Likewise, the evolution of the temperature, pH, dissolved oxygen, iron, and manganese in the filling and postfilling processes are shown. This second article presents the temporal evolution of other major components and nutrients during the filling period (2008–2016) and postfilling period (2016–2019). The continuation of the analysis initiated in the aforementioned article is done at certain heights of the vertical profiles monitored at the midpoint of the lake (the surface, the first 2 m of depth with respect to the surface (2 mbs), at 59 masl, and at the bottom (2 masl)). As explained in the filling process, an invariant chemocline and a seasonal thermocline near the water surface are formed. Therefore, the analysis encompasses not only the bottom and surface of the lake but also includes an intermediate point where the chemocline is found. Based on the analysis carried out, it has been possible to verify that the most superficial waters (80 m) are in line with the geological and fluvial environment of the basin, so that the stored waters do not need a special physicochemical treatment. However, at deeper levels, anoxization processes are developing, a step prior to the methanization of the lake bottom.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 23","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and field verification of Fe-bearing rocks in the Hasançelebi region (Malatya, Türkiye) and its vicinity using ASTER and Sentinel-2A images","authors":"Sedat İnal,&nbsp;Kaan Sevki Kavak","doi":"10.1007/s12665-024-11962-y","DOIUrl":"10.1007/s12665-024-11962-y","url":null,"abstract":"<div><p> In this study, image processing has been applied to ASTER and Sentinel-2A satellite images, and obtained data is used to reveal Fe-bearing rocks in the vicinity of Hasançelebi (Malatya), close to Divriği (Sivas) which is one of the most important iron provenances in the Central-Eastern Anatolia region of Türkiye. Remote sensing images, particularly the visible-near-infrared (VNIR) and partially shortwave infrared (SWIR) bands, have been employed to identify Fe-bearing minerals and rocks. With the purpose of identifying Fe-bearing minerals and rocks, various band rationing processes have been applied. Supervised classification which utilizes a parallelepiped algorithm has been employed on the resulting ratio images to create classification distributions for Fe-bearing minerals. According to the classification results; ferrous iron (Fe²⁺) and ferric oxides are more associated with ophiolitic rocks, ferrous silicates and ferric iron (Fe³⁺). The distributions are generally associated with clastic lithologies, and laterite and gossan appear to be associated with volcanic and plutonic rocks. Because of the different band widths in the VNIR range, Sentinel-2A classifications have the highest pixel count when compared to ASTER classifications for the same surface areas. During fieldwork, rock samples representing the lithologies and Fe-bearing minerals in the region have been collected and mineralogical-petrographic, geochemical, and XRD analyses have been conducted on these samples. Additionally, for spectral mineral identification and to compare Fe-bearing minerals with other analysis results, spectral signatures have also been obtained from the same samples via Analytical Spectral Device (ASD). In extracting features such as lineaments and faults, which play a crucial role in the development of ore deposits along the structural discontinuities, digital elevation models (DEM) have been preferred instead of optical images. When lineament analysis results and iron deposits, which had been identified in previous studies, were overlapped, it has been detected that revealed iron deposits are predominantly associated with the Ciritbelen-Otmangölü Fault (COF) which is an approximately east-west trending strike-slip fault located in the study area, along with other related fault systems. They are generally distributed within an ophiolitic slice and the surrounding magmatic intrusions.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel optimized coupled runoff model is developed based on the concept of “decomposition-prediction-reconstruction”","authors":"Xianqi Zhang,&nbsp;Yupeng Zheng,&nbsp;Yang Yang,&nbsp;Yike Liu,&nbsp;Kaiwei Yan","doi":"10.1007/s12665-024-11919-1","DOIUrl":"10.1007/s12665-024-11919-1","url":null,"abstract":"<div><p>Runoff refers to the quantity of water that flows over the surface of the ground from precipitation, snowmelt, or other sources, playing a crucial role in water resource management. Accurate runoff prediction in water resource modeling aids in managing water resources, forecasting floods and droughts, optimizing reservoir operations, and formulating reasonable water use policies. Advanced modeling techniques, enable more precise capture of the temporal characteristics of runoff, thereby improving the accuracy and reliability of predictions and playing a significant role in ensuring the sustainable use of water resources. To enhance the precision of runoff forecasts, a novel approach has been introduced. This methodology integrates the Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN) with a Bidirectional Long Short-Term Memory (BiLSTM) model, further optimized through the application of the Sparrow Search Algorithm (SSA). The coupling of the SSA-BiLSTM model has led to substantial optimization of several parameters, including the number of iterations, the quantity of hidden layer nodes, and the learning rate. The resulting model, termed the CEEMDAN-SSA-BiLSTM, offers an advanced and integrated solution for predicting both short-term and long-term runoff scenarios, thereby facilitating more effective water resource management and environmental preservation within the basin. Daily runoff data from 2016 to 2022 were analyzed at four hydrological stations—Huayuankou, Jiahetan, Gaocun, and Lijin. The approach involved using 80% of the daily runoff data for training and 20% for prediction. The performance of the CEEMDAN-SSA-BiLSTM model was compared against several other models, including LSTM, BiLSTM, and CEEMDAN-BiLSTM, using various evaluation indices. The error results for the CEEMDAN-SSA-BiLSTM model compared to the aforementioned models are as follows: For the HuaYuankou station, the RMSE is 97.42, the MAPE is 5.46%, the MAE is 56.9, and the NSE is 0.96. At the JiaHetan station, the RMSE is 950.36, the MAPE is 6.76%, the MAE is 59.33, and the NSE is 0.96. For the GaoCun station, the RMSE is 92.38, the MAPE is 5.53%, the MAE is 54.85, and the NSE is 0.97. Finally, for the LiJin station, the RMSE is 88.31, the MAPE is 6.49%, the MAE is 52.68, and the NSE is 0.95. The ultimate results indicate that the CEEMDAN-SSA-BiLSTM model demonstrates superior accuracy in forecasting daily runoff, with fewer errors relative to the other models.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting groundwater drawdown in Zakho region, Northern Iraq, using machine learning models optimized by the whale optimization algorithm","authors":"Youssef Kassem,&nbsp;Idrees Majeed Kareem,&nbsp;Hindreen Mohammed Nazif,&nbsp;Ahmed Mohammed Ahmed,&nbsp;Hashim Ibrahim Ahmed","doi":"10.1007/s12665-024-11923-5","DOIUrl":"10.1007/s12665-024-11923-5","url":null,"abstract":"<div><p>Predicting groundwater drawdown is crucial to the Duhok Governorate’s sustainable management of its water resources. To ensure long-term water availability as extraction from population growth and development intensifies, predicting drawdown helps to prevent overuse, provide a continuous supply of water, and enable effective planning for urbanization, agriculture, and industrial needs. In this work, a novel approach based on Multi-layer perceptron neural network (MLP), support vector regression (SVR), k-nearest neighbor algorithm (KNN), and extreme learning Machine (ELM) optimized by whale optimization algorithm (WOA) were proposed for estimating the total drawdown at Zakho region, Duhok Governorate, Northern Iraq for the first time. The input variables of the models include the rate of water extraction from the well (Q), well depth (D), and various meteorological parameters such as rainfall (R), evapotranspiration (E), Maximum Temperature (Tmax), and Minimum Temperature (Tmin). It is found that ELM showed the highest performance in modeling groundwater drawdown (R² = 0.911, RMSE = 5.674 m, and MAE = 4.937 m). Moreover, the novelty of the research work is to enhance the accuracy of the individual models using two ensemble techniques including simple averaging ensemble (SAE) and weighted average ensemble (WAE). Based on the findings, the WAE technique increased the performance of individual models by up to 20%, proving the reliability of the WAE technique for groundwater drawdown prediction.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of optical remote sensing data for automatic fracture network mapping in Lokoja region, Central Nigeria","authors":"Jamilu B. Ahmed II,&nbsp;Ernest O. Akudo,&nbsp;Kizito O. Musa,&nbsp;Usman S. Lay,&nbsp;Ojogbane S. Sani,&nbsp;Ikenna A. Obasi,&nbsp;Ibrahim Y. Anzacku,&nbsp;Godwin O. Aigbadon","doi":"10.1007/s12665-024-11958-8","DOIUrl":"10.1007/s12665-024-11958-8","url":null,"abstract":"<div><p>This study evaluates the efficiency of some open source optical remote sensing data (Landsat OLI, Sentinel-2 A, ASTER and DEM) in automatic lineaments extraction over Lokoja region of Central Nigeria. Various image processing techniques involving principal component analysis (PCA), directional filtering and shaded relief were employed followed by a robust lineament extraction technique and novel false lineaments filtration method. The result indicated significant variation in the number, length and accuracy of extracted lineaments across the datasets. Comparison of results by way of accuracy assessment was achieved after superimposition of extracted lineaments on geological map and shading map of the study area as well as by comparing their orientations with field obtained fracture data. Landsat OLI and Sentinel-2 A demonstrated better performance by extracting longer and more numerous lineaments many of which correlated with lithological boundaries and boundaries between shaded and unshaded areas. Orientations of the extracted lineaments indicated a majorly ENE-WSW and NE-SW trend while the field obtained fracture orientations showed a majorly NNW-SSE and NE-SW directions. All the images failed to identify lineaments along the NW-SE directions. The findings of this study underscored the importance of selecting appropriate datasets for regional geological investigations.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing 3D geological and geotechnical engineering model of Bangkok subsoil using optimal deep neural network models","authors":"Punthin Pintusorachai,&nbsp;Weeradetch Tanapalungkorn,&nbsp;Suched Likitlersuang","doi":"10.1007/s12665-024-11942-2","DOIUrl":"10.1007/s12665-024-11942-2","url":null,"abstract":"<div><p>Understanding the geotechnical characteristics of subsoil is important for safety and efficiency in design and construction processes. In particular, the subsoil in the Bangkok Metropolitan area has accumulated soft marine clay over a long period, resulting in a thick layer of soft clay, which poses challenges for engineers. This study presents an approach to modelling the subsoil of the Bangkok Metropolitan region by utilising a large dataset of borehole data, enhanced with a Deep Neural Network (DNN) model, to develop a 3D geotechnical map. The hyperparameters of the DNN were tuned to fit the dataset for classifying the soil layers and the regression models were generated to predict the geotechnical engineering properties of the Bangkok subsoil, including the bulk unit weight, water content, plasticity index, undrained shear strength, and SPT-N values. The DNN model performance has been evaluated to ensure the accuracy and reliability of its predictions. The generated 3D geotechnical map was compared with the map obtained from the traditional kriging method to verify the map accuracy and differences in results between these two approaches. This study demonstrates the potential of machine learning techniques for improving geotechnical mapping and geotechnical engineering information. The outcomes of this research also support Sustainable Development Goals (SDGs), particularly SDG 9, by providing accurate geotechnical data to enhance sustainable infrastructure planning, and SDG 11, by refining the subsoil model in urban areas, which contributes to safer and more sustainable urban development while reducing environmental risks in construction.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide susceptibility assessment in Eastern Himalayas, India: a comprehensive exploration of four novel hybrid ensemble data driven techniques integrating explainable artificial intelligence approach","authors":"Sumon Dey,&nbsp;Swarup Das,&nbsp;Sujit Kumar Roy","doi":"10.1007/s12665-024-11945-z","DOIUrl":"10.1007/s12665-024-11945-z","url":null,"abstract":"<div><p>In the field of landslide susceptibility, the utilization of data driven methodologies has seen a significant breakthrough. However, the performance of the models depends on the geo-environmental factors, and the selection of factors vary from one location to another, and this leads to a persistent lacuna for the present exploration. This study was aimed to assess landslide susceptibility for Darjeeling hills in Eastern Himalayan region with sixteen causative geo-environmental factors. The selection of causal factors was performed through a two-stage procedure, namely Pearson’s correlation coefficient (PCC) and Boruta algorithm (PCC-BA). The dataset associated with the research was split randomly into 70:30 ratio for train and test data. In addition, 30% of the training data was taken as validation dataset. Four advanced data-driven models namely K-nearest neighbour (KNN), Boosted Tree (BT), Gradient Boosting Machines (GBM) and ensembled Neural Network with Principal Component Analysis (PCA-NN) were taken up and four advanced novel ensembles namely KNN-BT, PCA-NN-BT, GBM-KNN and GBM-PCA-NN were constructed. The susceptibility maps were grouped into five divisions, viz., very low (VL), low (L), medium (M), high (H), and very high (VH) susceptibility. Through area under receiver operation characteristics curve, the accomplishment of constructed susceptibility models was substantiated with training, testing and validation dataset, where KNN-BT attained 0.943, 0.889 and 0.944 respectively, PCA-NN-BT attained 0.934, 0.876 and 0.943 respectively; GBM-KNN attained 0.959, 0.897 and 0.957 respectively; and GBM-PCA-NN attained 0.956, 0.889 and 0.962 respectively. The researchers have utilized an extensive explainable artificial intelligence (ex-AI) method, partial dependence profile (PDP) to quantify the effect of causal factors on all the four ensembled models. The study was aimed to demonstrate a significant capacity to substantially optimize disaster mitigation policies with a constituent endeavour to bridge the chasm between contemporary machine learning approaches and geo-spatial applications, and thereby paving the way to enhance the resilience of inhabitants in landslide prone areas of hilly portion of Darjeeling district.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interaction and the interface migration between salt lake water and fresh groundwater in small salt lake: a case of Lake Cherigele, Badain Jaran Desert, China","authors":"Le Cao,&nbsp;Weijia Liu,&nbsp;Zhongshuang Cheng,&nbsp;Xuequan Liu,&nbsp;Qian Wang","doi":"10.1007/s12665-024-11952-0","DOIUrl":"10.1007/s12665-024-11952-0","url":null,"abstract":"<div><p>The salt lakes in arid deserts serve as crucial ecological resources and tourist attractions. However, due to the limitations of aeolian sand cover, it is challenging to directly investigate the underground contact relationship between lake water and groundwater. In the Badain Jaran Desert (BJD) of China, the fresh groundwater near salt lakes is the sole source of drinking water. Consequently, a pressing concern arises regarding how groundwater exploitation impacts the intrusion of salt water into groundwater. In this study, Lake Cherigele (CRG) was chosen as a case to investigate the characteristics and migration mechanisms of the saltwater-freshwater interface using numerical simulation methods. The results reveal that: (1) The saltwater-freshwater mixing zone exhibits a wedge-shaped morphology, with a length of 250 to 290 m and a depth ranging from 50 to 70 m within the model. (2) The hydrodynamic conductivity coefficient (<i>K</i>) of the sand layer and molecular diffusion coefficient (<i>D</i>_<i>m</i>) in the desert are identified as the primary parameters influencing the characteristics of the interface. (3) The annual fluctuations in the current groundwater level have a limited impact on the saltwater-freshwater interface; however, excessive groundwater exploitation can lead to the intrusion of salt water into freshwater body. (4) The density difference between groundwater and lake water results in the concentration of groundwater flow lines around the lake, giving rise to the numerous springs observed in the field.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling water table depth fluctuation with special reference to iran: the singular spectrum analysis approach","authors":"Mehrdad Barati,&nbsp;Rahim Mahmoudvand,&nbsp;Asghar Seif,&nbsp;Sahar Ranjbarian,&nbsp;Faezeh Moazzez","doi":"10.1007/s12665-024-11924-4","DOIUrl":"10.1007/s12665-024-11924-4","url":null,"abstract":"<div><p>This paper presents a statistical analysis of water table depth fluctuations in the Ghahavand plain of Hamedan province, western Iran. The study is based on 49,232 water table depth records collected by the Hamedan regional water authority between October 1988 and March 2016. The data were processed to improve their quality and construct a monthly time series of water table depth. Singular Spectrum Analysis (SSA), a non-parametric technique for time series analysis, was used to analyze the time series. Exponentially Weighted Moving Average (EWMA) was applied to the residuals of the SSA model to monitor management policies and institutions. The EWMA control chart is an effective alternative to the Shewhart control chart when the goal is to detect smaller shifts in the process. One of the key advantages of using EWMA is its robustness to non-normality, making it suitable for real-world applications where data often deviates from the normal distribution. The results indicate that the SSA model fits the data very well. Using EWMA control chart on the residuals of the SSA model revealed three statistically unusual points that need to be examined by water resource management systems. The findings of this study can assist water resource managers in making informed decisions regarding groundwater management policies and practices.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}