Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Assessment of sodium adsorption ratio (SAR) in groundwater: Integrating experimental data with cutting-edge swarm intelligence approaches","authors":"Zongwang Wu, Hossein Moayedi, Marjan Salari, Binh Nguyen Le, Atefeh Ahmadi Dehrashid","doi":"10.1007/s00477-024-02727-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02727-x","url":null,"abstract":"<p>In developing countries, evaluating irrigation water quality using conventional methods can be costly and time-consuming. To overcome these challenges, this study explores the potential of utilizing physical parameters and artificial intelligence (AI) models for predicting and evaluating the quality indicators of irrigation water in aquifer systems. To achieve this goal, novel hybrid methods, namely the Whale Optimization Algorithm (WOA) and Wind-Driven Optimization (WDO), are employed in conjunction with Artificial Neural Network (ANN) models. The specific objective of this study is to forecast the Sodium Adsorption Ratio (SAR) by considering independent variables such as Na⁺, Mg²⁺, Ca²⁺, Na percent, K⁺, SO₄²⁻, Cl⁻, pH, and HCO₃⁻. A dataset of 540 samples from the Shiraz plain, collected over a statistical period of 16 years (2002–2018), is used to estimate the groundwater quality variables. A pre-processing technique is applied in the AI approach to enhance the model's efficiency. The results indicate that the WDO-ANN model exhibits higher accuracy (R² = 0.9983 and RMSE = 0.10618) than the WOA-ANN model (R² = 0.9957 and RMSE = 0.16957). The optimization of computational parameters and comparison of AI model structures demonstrate that the WDO-ANN model outperforms the WOA-ANN model in predictive ability. In general, using AI models as a tool for low-cost and timely prediction of underground water quality using physical parameters as input variables has a high potential.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"20 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 seasonal binomial autoregressive process with applications to monthly rainy-days counts","authors":"Yao Kang, Feilong Lu, Danshu Sheng, Shuhui Wang","doi":"10.1007/s00477-024-02718-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02718-y","url":null,"abstract":"<p>Count time series exhibiting seasonal patterns are frequently encountered in practical scenarios. For example, the number of hospital emergency service arrivals may show seasonal behavior (Moriña et al. 2011 Stat Med 30:3125–3136). Numerous models have been proposed for the analysis of seasonal count time series with an unbounded support, yet seasonal patterns in bounded count time series, which are sometimes suffered in environmental science such as the number of monthly rainy-days and air quality level data, have not received formal attention. The contribution of this article lies in coping with the modeling challenges associated with seasonal count time series with a bounded support, which is beneficial for enhancing the applicability of environmental science data. This is achieved by introducing a seasonal structure and seasonally varying model parameters into the first-order binomial autoregressive (BAR(1)) model (McKenzie 1985 J Am Water Resour Assoc 21:645–650). The probabilistic and statistical properties, marginal distribution and some special cases of the proposed model are studied. Estimation of model parameters is conducted using the Yule-Walker, conditional least squares and maximum likelihood methods. The asymptotic normality of the estimators is also presented. To demonstrate the utility of our model in environmental data, applications are carried out on the monthly number of rainy-days in two Russian cities.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"22 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Enhanced monthly streamflow prediction using an input–output bi-decomposition data driven model considering meteorological and climate information","authors":"Qiucen Guo, Xuehua Zhao, Yuhang Zhao, Zhijing Ren, Huifang Wang, Wenjun Cai","doi":"10.1007/s00477-024-02731-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02731-1","url":null,"abstract":"<p>Accurate streamflow prediction is significant for water resources management. However, due to the impact of climate change and human activities, accurately identifying the input factors of the streamflow prediction model and achieving high-precision results presents a significant challenge. In this study, past streamflow, meteorological, and climate factors were utilized as inputs to develop a predictive scenario for the bi-decomposition of input factors and streamflow series, i.e. Scenario 3 (S3). Mutual information (MI) was applied to recognize the input factors prediction potential. Based on the predictive potentials, factors were progressively incorporated into the kernel extreme learning machine (KELM) and hybrid kernel extreme learning machine (HKELM) models optimized by the gazelle optimization algorithm (GOA) to ascertain the optimal input configuration for each sub-series. The prediction results of S3-KELM and S3-HKELM models were obtained by reconstructing the optimal prediction results of each sub-series. The monthly streamflow of the upper Fenhe River Basin, which is in the semi-humid and semi-arid climate zone, was selected as a case study. The results indicate that in comparison to both undecomposed and singly decomposed scenarios, the input–output bi-decomposed scenario more accurately identifies the input factors and constructs high-precision prediction models. The Nash–Sutcliffe efficiency (NSE) of both the S3-KELM and S3-HKELM models exceeds 0.85. Specifically, the S3-HKELM model demonstrates superior performance, capable of handling more complex inputs, with its NSE reaching up to 0.93. Importantly, meteorological and climate factors contribute to the accuracy of streamflow predictions across different scenarios.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"56 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140804733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 generative adversarial neural network with multi-layers stack ensemble hybrid model for landslide prediction in case of training sample imbalance","authors":"Wajid Hussain, Hong Shu, Hasnain Abbas, Sajid Hussain, Isma Kulsoom, Saqib Hussain, Hajra Mustafa, Aftab Ahmed Khan, Muhammad Ismail, Javed Iqbal","doi":"10.1007/s00477-024-02722-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02722-2","url":null,"abstract":"<p>Gilgit-Baltistan, Pakistan, is particularly susceptible to landslides due to various geological, tectonics, meteorological, and anthropogenic factors consequently. However, the persisting conundrum of landslide database/data imbalance stands as a formidable challenge within this domain. To better stabilize the objective of landslide prediction, stacking ensemble Machine Learning and Generative Adversarial Network (GAN) were applied, because previous research in this area has mostly been limited by a lack of data. GAN is employed to synthesize training samples, ensuring the creation of a balanced dataset. Stacking ensemble architecture involves two stages of learning: the first class of learners incorporates diverse machine learning algorithms, while, the second level logistic regression model integrates prediction based on the strong learner, thereby enhancing overall prediction performance. To investigate landslide susceptibility in District Chilas, Northern Pakistan, we employed optical remote sensing and introduced a GAN with a Multi-Layers Hybrid Model (MLHM). This study involved the preparation of a spatial database with a total of 106 landslides and ten major landslide factors. We utilized a hybrid ensemble model and compared its performance with different algorithms like Conventional Neural Network, Artificial Neural network, Decision Tree, K-Nearest Neighbouring, and Hybrid Model, achieving accuracies of 0.91, 0.92, 0.90, 0.89, and 0.93, respectively. this approach has with Hybrid architecture learning accuracy of 0.98. The GAN with MLHM developed improved landslide susceptibility assessment with cross-comparison of Persistent Scattered Interferometric Synthetic Aperture Radar (PS-InSAR) investigation to ensure the safe functioning of KKH. </p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"71 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Inhomogeneous log-Gaussian Cox processes with piecewise constant covariates: a case study in modeling of COVID-19 transmission risk in East Java","authors":"Alwan Fadlurohman, Achmad Choiruddin, Jorge Mateu","doi":"10.1007/s00477-024-02720-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02720-4","url":null,"abstract":"<p>The inhomogeneous Log-Gaussian Cox Process (LGCP) defines a flexible point process model for the analysis of spatial point patterns featuring inhomogeneity/spatial trend and aggregation patterns. To fit an LGCP model to spatial point pattern data and study the spatial trend, one could link the intensity function with continuous spatial covariates. Although non-continuous covariates are becoming more common in practice, the existing estimation methods so far only cover covariates in continuous form. As a consequence, to implement such methods, the non-continuous covariates are replaced by the continuous ones by applying some transformation techniques, which are many times problematic. In this paper, we develop a technique for inhomogeneous LGCP involving non-continuous covariates, termed piecewise constant covariates. The method does not require covariates transformation and likelihood approximation, resulting in an estimation technique equivalent to the one for generalized linear models. We apply our method for modeling COVID-19 transmission risk in East Java, Indonesia, which involves five piecewise constant covariates representing population density and sources of crowd. We outline that population density and industry density are significant covariates affecting the COVID-19 transmission risk in East Java.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"1 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Modelling multidecadal variability in flood frequency using the Two-Component Extreme Value distribution","authors":"Vincenzo Totaro, Andrea Gioia, George Kuczera, Vito Iacobellis","doi":"10.1007/s00477-024-02673-8","DOIUrl":"https://doi.org/10.1007/s00477-024-02673-8","url":null,"abstract":"<p>The Two-Component Extreme Value (TCEV) distribution is traditionally known as the exact distribution of extremes arising from Poissonian occurrence of a mixture of two exponential exceedances. In some regions, flood frequency is affected by low-frequency (decadal) climate fluctuations resulting in wet and dry epochs. We extend the exact distribution of extremes approach to such regions to show that the TCEV arises as the distribution of annual maximum floods for Poissonian occurrences and (at least two) exponential exceedances. A case study using coastal basins in Queensland and New South Wales (Australia) affected by low-frequency climate variability, shows that the TCEV produces good fits to the marginal distribution over the entire range of observed values without the explicit need to resort to climate covariates and removal of potentially influential low values. Moreover, the TCEV reproduces the observed <i>dog-leg</i>, a key signature of different flood generation processes. A literature review shows that the assumptions underpinning the TCEV are conceptually consistent with available evidence on climate and flood mechanisms in these basins. We provide an extended domain of the TCEV distribution in the L-moment ratio diagram to account for the wider range of parameter values encountered in the case study and show that for all basins, L-skew and L-kurtosis fall within the extended domain of the TCEV.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"5 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140625413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Risks of heat waves in South Korea using structural equation modeling and entropy weighting","authors":"Dongwook Kim, Ji Eun Kim, Juil Song, Sang Won Lee, Jae-Hyun Ahn, Tae-Woong Kim","doi":"10.1007/s00477-024-02725-z","DOIUrl":"https://doi.org/10.1007/s00477-024-02725-z","url":null,"abstract":"<p>Heat waves are natural disasters that can result in large numbers of casualties. The frequency and damage caused by heat waves have been increasing in Korea due to climate change. The regional impacts of heat waves can vary according to environmental and socioeconomic factors regardless of duration and intensity. This study assessed the risks posed by heat waves for administrative districts in Korea according to climate change scenarios and the risk assessment framework of Fifth Assessment Report presented by the Intergovernmental Panel on Climate Change. The risk of heat waves is usually based on a combination of hazard, exposure, and vulnerability. Unlike previous studies using subjective weights, this study employed partial least squares—structural equation modeling (PLS-SEM) and entropy weighting, which are more objective methods of determining the indicators and weights, to estimate the exposure and vulnerability of heat waves. The results showed that at least 40% and 46% of administrative districts are expected to experience a high level of risk according to the representative concentration pathway scenarios, i.e., RCP 4.5 and 8.5, respectively. In addition, significant differences were observed in the heat wave risks calculated in this study for the upper and lower regions, with respect to cumulative heat-related morbidity rates, whereas the heat wave risk reported by the Korean Ministry of Environment was found to be insignificant. The results of this study can be used to prepare for heat waves and minimize damage caused by them.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"49 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics","authors":"Aamina Batool, Zulfiqar Ali, Muhammad Mohsin, Atef Masmoudi, Veysi Kartal, Samina Satti","doi":"10.1007/s00477-024-02721-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02721-3","url":null,"abstract":"<p>Climate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 new approach for hydrograph data interpolation and outlier removal for vector autoregressive modelling: a case study from the Odra/Oder River","authors":"Michał Halicki, Tomasz Niedzielski","doi":"10.1007/s00477-024-02711-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02711-5","url":null,"abstract":"<p>This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in <span>(varvec{approx })</span> 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"22 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 semi data dimension reduction type weighting scheme of the multi-model ensemble for accurate assessment of twenty-first century drought","authors":"Alina Mukhtar, Zulfiqar Ali, Amna Nazeer, Sami Dhahbi, Veysi Kartal, Wejdan Deebani","doi":"10.1007/s00477-024-02723-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02723-1","url":null,"abstract":"<p>Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"38 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}