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- An optimally improved entropy weight method integrated with a fuzzy
comprehensive evaluation for complex environment systems-
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Abstract: An effective evaluation method for analyzing the actual environmental multi-index systems is an important task. However, the inherent complexity and interrelatedness of environmental factors pose substantial challenges to this task. Many methods have been proposed and verified to solve this problem. However, unreasonable weight determination and sole single-index assessments limit the practical application of these analysis methods. In this study, we propose an optimally improved entropy weight calculation method (OIEW) that combines mathematical programming with the principle of consistency in entropy weight variation to determine the weight of each index. The simulation results demonstrate that our proposed method enhances robustness against extreme data while also effectively mitigating the over-correction of normal data during the weight determination process. Furthermore, by combining the OIEW method with the fuzzy comprehensive evaluation (FCE) method, the OIEW-FCE approach can be utilized to evaluate soil quality grades. Soil data from ten Chinese provinces are selected as the research specimens in this paper. An evaluation system for soil physical and chemical properties is developed, comprising two first-grade indices and six second-grade indices. The evaluation results show that the OIEW-FCE method significantly reduced the overall error in soil grade evaluation by approximately 20% compared to the improved entropy weight evaluation system utilizing the FCE and the OIEW methods employing the single factor evaluation method. This result indicates that our evaluation system can maintain accuracy and reliability in practical applications. Our method quantitatively assesses performance deviations from actual soil usage scenarios and has potential applications in relevant fields, such as environmental impact assessment, ecological resource management, and sustainable development planning.
PubDate: 2025-04-05
- Using a random forest model for cross-species prediction of crop arsenic
contamination-
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Abstract: Arsenic (As), a harmful metalloid, presents serious risks to both the environment and human health because of its high toxicity and widespread presence. Human exposure to As primarily occurs through polluted water consumption and the ingestion of food with high As content. The As levels in crops are determined not only by the As content in soil but also by the interactions of As with other soil elements. In this research, the Qinghai-Tibet Plateau was chosen as the research area. Using the random forest (RF) algorithm, Sr and leachable Pb were identified from 29 soil indicators to predict As levels in rapeseed, wheat, potato, grass, and chicory crops. The results show that, compared to traditional multiple linear regression methods, the RF model offers higher accuracy and precision in predicting crop As content. In particular, in cross-species prediction, RF models have demonstrated excellent predictive performance. This study marks the first successful attempt at cross-species research using this model. This approach avoids the requirement for redundant evaluations of various crop types within the same region, signifying significant innovation. Moreover, the unique environment of the Qinghai‒Tibet Plateau increases the value of this research. The findings provide valuable insights for effective farmland planning and management, facilitating better organization of crop cultivation areas.
PubDate: 2025-04-04
- Modeling multiday extreme precipitation across eastern Australia: a
dynamical perspective-
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Abstract: The purpose of this paper is to illustrate new techniques for computing multiday extreme precipitation taken from recent theoretical advancements in extreme value theory in the framework of dynamical systems, using historical precipitation data along the eastern coast of Australia as a case study. We explore the numerical pitfalls of applying standard extreme value techniques to model multiday extremes. Then, we illustrate that our data conforms to the appropriate setting for the application of recently derived extreme value distributions for runs of extremes in the dynamical framework and adapt these to the nonstationary setting. Finally, we use these distributions to make more informed predictions on the return times and magnitudes of consecutive daily extreme precipitation and find changes in the dependence of increasing consecutive daily rainfall extremes on the Southern Oscillation Index. Although our case study is focused on extreme precipitation across eastern Australia, we emphasize that these techniques can be used to model expected returns and magnitudes of consecutive extreme precipitation events across many locations.
PubDate: 2025-04-03
- Nonparametric Bayesian Poisson hurdle random effects model: an application
to temperature–suicide association study-
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Abstract: In environmental epidemiology, the short-term association between temperature and suicide has been examined by analyzing daily time-series data on suicide and temperature collected from multiple locations. A two-stage meta-analytic approach has been conventionally used. A Poisson regression with splines is fitted for each location in the first stage, and location-specific association parameter estimates are pooled, adjusted, and regressed onto location-specific variables using meta-regressions in the second stage. However, several limitations of the conventional two-stage approaches have been reported. First, the Poisson distribution assumption may be inappropriate because the daily number of suicides is often zero. Second, the normal assumption in the second-stage meta-regression is not sufficiently flexible to describe between-location heterogeneity when subgroups exist. Third, the two-stage approach does not properly account for the statistical uncertainty associated with first-stage estimates. In this study, we propose a nonparametric Bayesian Poisson hurdle random effects model to investigate heterogeneity in the temperature–suicide association across multiple locations. The proposed model consists of two parts, binary and positive, with random coefficients specified to describe heterogeneity. Furthermore, random coefficients combined with location-specific indicators were assumed to follow a Dirichlet process mixture of normals to identify the subgroups. The proposed methodology was validated through a simulation study and applied to data from a nationwide temperature–suicide association study in Japan.
PubDate: 2025-04-02
- Financialization and environmental policy as drivers of environmental
technology in OECD economies-
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Abstract: The United Nations’ Sustainable Development Goals urge a combined focus on economic development that account for improved environmental quality, thus prompting increased attention on the advancement of environmental-related technologies and innovations. Financialization (instrumentation of financial development, markets, and institutions), environmental policy, and trade openness are critical facets driving this agenda. While numerous factors influencing environmental technology advancement have been explored, the interplay of financialization and environmental technology growth is further advanced. This research examines this relationship across selected 26 Organisation for Economic Co-operation and Development (OECD) nations from 2000 to 2021 employing the 2-step system generalized method of moment (GMM) techniques. The results show that environmental policy, financial institutions, and markets foster environmental technology growth, while financial development and trade openness impede it. The study further unveils environmental policy effectively which facilitates the positive impact of financial development, markets, and institutions on environmental technology growth in these nations. Thus, this finding further hints on the relevance of financial institutions and adoption of environmental policy to the improvement of environmental-related technologies among the OECD economies. Moreover, effective environmental policies are essential to enhance the positive impacts of financial systems, directing resources toward sustainable investments and cleaner technologies. Integrating these policies with financial and trade strategies is crucial for achieving environmental sustainability objectives.
PubDate: 2025-03-28
- A frequentist approach on fixed effects estimation for spatially
confounded regression models-
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Abstract: In spatial regression analysis, the confounding between fixed and random effects can lead to biased estimation of regression coefficients. This paper proposes a novel estimation methodology that leverages the fixed rank kriging approach to mitigate these biases. A key advantage of the proposed method is that it circumvents the need for parametric assumptions about the covariance structure of the response variable, enhancing its practical applicability. The estimation process involves selecting an appropriate number of basis functions, which balances bias and variance in the estimators. To minimize the mean squared error of the estimators, we introduce two approaches: a bootstrap aggregation estimator and a $$\gamma$$-estimator. Theoretical properties of the proposed methodology are explored and justified. Extensive simulation studies under various spatial regression settings, including cases of spatial confounding and different correlation structures such as stationary, nonstationary, isotropic, and anisotropic, demonstrate the robustness of the proposed methods. Finally, the methodology is applied to a case study on precipitation data from Colorado, which highlights its practical effectiveness.
PubDate: 2025-03-27
- A computationally efficient procedure for combining ecological datasets by
means of sequential consensus inference-
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Abstract: In ecology and environmental sciences, combining diverse datasets has become an essential tool for managing the increasing complexity and volume of ecological data. However, as data complexity and volume grow, the computational demands of previously proposed models for data integration escalate, creating significant challenges for practical implementation. This study introduces a sequential consensus Bayesian inference procedure designed to offer the flexibility of integrated models while significantly reducing computational costs. The method is based on sequentially updating some model parameters and hyperparameters, and combining information about random effects after the sequential procedure is complete. The implementation of the approach is provided through two different algorithms. The strengths, limitations, and practical use of the method are explained and discussed throughout the methodology and examples. Finally, we demonstrate the method’s performance using two different examples with real ecological data, highlighting its strengths and limitations in practical ecological and environmental applications.
PubDate: 2025-03-11
- Non-separable spatio-temporal Poisson point process models for fire
occurrences-
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Abstract: Our study addresses the analysis of environmental concerns through point process theory. Among those, Sicily faced an escalating issue of uncontrolled fires in recent years, necessitating a thorough investigation into their spatio-temporal dynamics. Each fire is treated as a unique point in both space and time, allowing us to assess the influence of environmental and anthropogenic factors. A non-separable spatio-temporal Poisson model is applied to investigate the influence of land use types on fire distribution, controlling for other environmental covariates. The results highlight the significant effect of human activities, altitude, and slope on spatio-temporal fire occurrences, also confirming their dependence on various environmental variables, including the maximum daily temperature, wind speed, surface pressure, and total precipitation. As a model with constant parameters in space and time may be too restrictive, a local version of the proposed model is also fitted. This allows us to obtain better performance and more valuable insight into the estimated effects of the different environmental covariates on the occurrence of fires, which we find to vary both in time and space. This research work’s relevance lies in the analysis of an important environmental problem through complex point process models, yet easily interpretable, given their resemblance to regression-type models. We also provide reference to newly available open-source software for estimating such models. Finally, we contribute to the framework of spatio-temporal point process modelling by integrating data with different spatio-temporal resolutions from very diverse sources.
PubDate: 2025-03-06
- Larval fish abundance classification and modeling through spatio-temporal
point processes approach-
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Abstract: Starting from the evaluation of presence-only data, and according to stochastic processes theory, we propose a classification method for unknown larval fish specimens, which is based on Local Indicators of Spatio-Temporal Association (LISTA). LISTA functions are typically used to evaluate the presence of clustered local second-order structures in spatio-temporal data. Here, these tools were applied to the classification of two rare species of mesopelagic fish larvae belonging to the genus Vinciguerria (V. attenuata and V. poweriae), detected in the Strait of Sicily, from 1998 to 2016. To evaluate the dependence of larval fish abundance spatio-temporal distributions from covariates, with the aim of understanding their impact on the reproducing activity of Vinciguerria spp., we fit a thinned inhomogeneous multitype spatio-temporal Poisson point process model. According to the goodness-of-fit evaluation, based on second-order diagnostics, the spatio-temporal Poisson point process model perfectly fits larval fish abundance’ presence-only data, after the classification procedure. We classify units representing spatio-temporal events by a LISTA functions-based classification procedure of local interaction. In addition, a stochastic processes’ model for the evaluation of presence-only data from an inferential point of view is estimated, accounting for covariates and sampling bias correction. The modeling analysis is carried out before and after the classification procedure, with the aim to evaluate the difference in terms of interpretation and diagnostics.
PubDate: 2025-02-27
- Enhancing seasonal streamflow prediction using multistage hybrid
stochastic data-driven deep learning methodology with deep feature
selection-
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Abstract: This study proposes a novel multistage hybrid stochastic data-driven deep learning (DL) methodology with deep feature selection (DFS) for enhancing long-term seasonal streamflow prediction. The multistage hybrid MCMC-BC-DFS-BiLSTM-BiGRU model integrates bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) architectures with Markov Chain Monte Carlo (MCMC)-based bivariate copulas (BC) within the DFS framework. Firstly, a DFS strategy was developed using the Ant Colony Optimization (ACO) algorithm, autocorrelation function (ACF), partial autocorrelation function (PACF), and recursive feature elimination (RFE) method to determine optimized predictor variables (PV) from a pool of three predictor candidates, i.e., local meteorological variables, large-scale atmospheric indices, and solar activity indices. Subsequently, several diverse MCMC-BC models were employed using optimal PV obtained through the DFS framework (MCMC-BC-DFS) to evaluate the connection between the current seasonal streamflow and its potential future variations. Finally, the most suitable MCMC-BC-DFS model was incorporated into the hybrid BiLSTM-BiGRU model to predict spring (Sep–Nov) streamflow across nine catchments in the Victorian site of the Upper Murray Basin (UMB), Australia. The proposed multistage hybrid MCMC-BC-DFS-BiLSTM-BiGRU model was compared to several machine learning (ML) models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), support vector machine (SVM), and random forest (RF)) through different robust statistical metrics and graphical illustrations. According to experimental findings, the proposed model demonstrated excellent performance in long-term streamflow prediction at the seasonal timescale by outperforming its benchmark counterparts based on all statistical indicators. Consequently, as a pioneer study, the proposed multistage hybrid model can be effectively utilized as a highly promising tool for upstream seasonal predictions that could be helpful to policymakers for more informed environmental decision-making.
PubDate: 2025-02-23
- Arbitrarily shaped spatial cluster detection via reinforcement learning
algorithms-
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Abstract: Studies on spatial cluster patterns are of interest in many areas. Spatial scan statistics is the most widespread strategy for studying these patterns. However, scan statistics lose substantial efficiency in situations where candidate clusters can assume irregular shapes. Conversely, other techniques, with the aim of increasing the flexibility of analyzing cluster shapes, have emerged. We present two novel reinforcement learning approaches that use scan spatial statistics to represent the reward function. The novel approaches are explained in detail, and there is an extensive set of computational experiments with controlled synthetic data to verify their functionality and adaptation to the problem of detecting spatial clusters. Our results attest to the quality and applicability of the new techniques for addressing this problem.
PubDate: 2025-02-22
- Estimating effects of ocean environmental conditions on summer flounder
(Paralichthys dentatus) distribution-
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Abstract: The relative abundance of summer flounder (Paralichthys dentatus) differs over space and time with changes in environmental factors, such as depth, bottom temperature, sea surface temperature (SST) and bottom salinity. We use the integrated nested Laplace approximation (INLA) approach to account for the random effects arising from either over-dispersion, or spatial and temporal autocorrelation. We explore how the different assumptions in the spatial temporal models result in varying model predictions. The results indicate that the distribution of summer flounder is correlated with depth, regional increases in bottom temperature, SST and bottom salinity. We find that in the Fall relative abundance increased 10–15% with a $$1^{\circ }$$C increase in SST, by 12% with each $$1^{\circ }$$C increase in bottom temperature and 3–4% with each meter increase in depth across all models. In the spring, relative abundance increased by about 30% with each $$1^{\circ }$$C increase in SST with an upper preferred temperature between $$10-20^{\circ }$$C. Our study also shows that models that include spatio-temporally correlated variables can inadvertently be over parameterized when including higher order interaction terms between spatial and temporal random effects. This can lead to inflated variances in the estimates and predictions as well as lengthening model convergence times. Therefore, care should be taken in identifying the level of model complexity given the indirect implications of these results on fisheries management and marine ecology.
PubDate: 2025-02-18
- Spatial-temporal seismicity analysis using TSOM and variational density
peak clustering-
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Abstract: Seismicity de-clustering is a crucial step in earthquake catalog analysis, essential for understanding earthquake patterns and assessing seismic hazards. Seismicity de-clustering is challenging due to complex geological structures, high spatial-temporal correlation between events, and large amounts of noise. This study proposes an innovative two-stage approach for spatial zone identification and seismicity de-clustering by leveraging the topological self-organizing map (TSOM) method and the variational density peak clustering (VDPC) algorithm. In the first stage, the TSOM method is employed with split-and-merge algorithm to identify spatial interactions in space domain to uncover intricate spatial relationships among earthquake events in two-dimensional space, allowing the effective separation of distinct seismic zones. In the second stage, temporal separation is performed using the VDPC algorithm to distinguish mainshocks from aftershocks further within each seismic zone. This two-stage model enhances the precision of seismicity de-clustering and provides a comprehensive understanding of earthquake catalog dynamics. The results obtained from the proposed model outperform the other benchmark de-clustering algorithms, validated using various statistical tests, including the coefficient of variation, m-Morisita index, cumulative plot, $$\lambda$$-plot and nearest neighbor distance. This innovative methodology enhances our comprehension of seismicity patterns, facilitates more accurate seismic hazard assessment, and ultimately contributes to improved earthquake preparedness in regions susceptible to seismic activity.
PubDate: 2025-02-18
- An accumulation rate curve estimator for total species
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Abstract: In this paper we present a total species estimator based on modelling the rate of change of a species accumulation curve (SAC). The proposed approach calculates an accumulation rate curve (ARC) for new species conditional on observed data and extrapolates it using parametric functions with varying rates of decay. The curve fits are integrated to obtain estimates for undetected species and a weighted estimate is calculated by optimizing a loss function subject to a set of restrictions. Confidence intervals are evaluated using a parametric bootstrap of aggregate counts, with the underlying count covariances estimated from a regularized mixture distribution fit to observed count data. A data smoothing technique and adjusting for bias are also discussed. The method is tested using a simulation study and applied to two example datasets. The results indicate that the proposed method is robust in a majority of cases and outperforms existing methods in bias and mean squared error. Performance is especially improved when the proportion of unobserved species is high. Confidence interval coverage is noticeably better compared to existing methods and conservative interval widths are maintained. The smoothing technique is also shown to be effective in reducing mean squared error under certain conditions.
PubDate: 2025-02-18
- Parameter estimation and model selection for stochastic differential
equations for biological growth-
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Abstract: In this paper, we consider the stochastic versions of three classical growth models given by ordinary differential equations (ODEs). Indeed we use the stochastic versions of Gompertz, von Bertalanffy, and logistic differential equations as models. We assume that each stochastic differential equation (SDE) has some crucial parameters to be estimated, and we use maximum likelihood estimation (MLE) to estimate them. For estimating the diffusion parameter, we use the MLE for two cases and the quadratic variation of the data for one of the SDEs. We apply the Akaike information criterion (AIC) to choose the best model for the simulated data. We consider that the AIC is a function of the drift parameter. We conduct numerical experiments to validate our selection method. Subsequently, we also apply it to actual data. The proposed methodology could be applied to datasets with discrete observations, including highly sparse data. Indeed, we can use this method even in the extreme case where we have observed only one point for each path, under the condition that we observed a sufficient number of trajectories. For the last two cases, the data can be viewed as incomplete observations of a model with a tractable likelihood function; then, we propose a version of the expectation maximization (EM) algorithm to estimate these parameters. This type of dataset typically appears in fishery, for instance.
PubDate: 2025-02-18
- Comparing methods for forecasting time series with multiple observations
per period using singular spectrum analysis-
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Abstract: In practice, a univariate time series typically represents the value of a quantitative variable in successive order over a period of time. However, in certain fields like hydrology, multiple observations may be available for each time point. Commonly, functions such as the average, maximum, and minimum are used to summarize these observations into a univariate time series, potentially losing valuable information. This paper proposes an alternative approach by constructing a time series of distributions from the observations. We explore two methods: (1) a non parametric approach using boxplots to create a time series of boxplots, and (2) a parametric approach using a parametric distribution, where the parameters of the distribution form the time series. These methods allow for the application of multivariate time series analysis techniques to better capture the underlying information. To demonstrate the practical application of these approaches, we employ singular spectrum analysis to model real climate change data from Europe.
PubDate: 2025-02-15
- A poisson cokriging modeling of mosquito-borne diseases in Colombia
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Abstract: Mosquito-borne diseases pose a significant public health concern in Colombia, necessitating robust quantification of their geographic patterns to guide and optimize interventions. This study explores the spatial dynamics and interactions among Zika, Dengue, and Chikungunya within the context of joint disease modeling in the Andean region of Colombia. Leveraging the Poisson cokriging method, we modeled and mapped an improved version of risks associated with the three diseases by incorporating a related mosquito-borne disease as secondary information while accounting for heterogeneous population distributions. Our findings reveal similar disease spatial risk patterns, suggesting possible shared localized transmission dynamics among the three diseases, with hotspots primarily occurring in municipalities characterized by high co-morbidity rates. The semivariogram and cross-semivariogram ranges suggested the potential influence of common local risk factors that might contribute to the spatial variation across the region. The smoothed disease risk maps highlight areas with elevated incidence rates, informing targeted intervention strategies. This study provides insights into the spatial distribution of the risk of Zika, Dengue, and Chikungunya, and hypothesize possible shared factors that drive their emergence in Colombia. It further highlights the utility of Poisson cokriging for improving disease risk mapping when auxiliary disease data are available, advancing the understanding of the intricate spatial relationships between related diseases.
PubDate: 2025-02-13
- Wavelet kernel estimation of spatiotemporal models applied to ozone
analysis in East China-
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Abstract: Near surface ozone pollution has become one of the biggest challenges in China’s air quality management. In order to better study the spatiotemporal heterogeneity of near surface ozone pollution, this paper takes the East China region as an example, uses a spatiotemporal weighted variable coefficient regression model, and uses ozone measurement data from the summer of 2021 to investigate the spatiotemporal evolution and heterogeneity of ozone concentration. In this work, we propose an innovative method to achieve better regression fitting results. We utilize the excellent properties of wavelet functions to create different wavelet kernels and develop wavelet kernel estimation packages for use. Due to the diversity of wavelet kernels, the most suitable wavelet kernel can be iteratively selected to achieve better model fitting based on the unique characteristics of different spatiotemporal data. We found that based on local weighting of spatial and temporal dimensions, this method can handle complex multidimensional spatiotemporal domains. As shown in simulation studies, the goodness of fit and mean square error of wavelet kernel regression for simulated data may be superior to the regression results of Gaussian kernel and double square kernel in certain situations. We further emphasized the potential of environmental problem methods by applying wavelet kernel estimation of spatiotemporal data to the analysis of ozone concentration, and found that longitude, latitude, and time can affect the relationship between ozone concentration, air quality, and nitrogen dioxide.
PubDate: 2025-01-10
- Atmospheric NO2 concentration prediction with statistical and hybrid deep
learning methods-
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Abstract: Recently, air pollution has become a critical environmental problem in Türkiye as well as in the world. Therefore, governments and scientists are putting a lot of effort into controlling air pollution and reducing its effects on human society. Scientists propose various models and methods for air quality forecasting because accurate estimation of air quality can provide basic decision-making support. This study proposes innovative hybrid models that integrate a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) neural network and a Gated Recurrent Unit (GRU) to predict one day ahead of NO2 concentration. For this aim, the Time-Series Daily NO2 concentration data obtained between 2015 and 2022 at the Istanbul and Ankara provinces in Türkiye are used. The hybrid CNN-LSTM and CNN-GRU models are compared with various traditional statistical and machine-learning methods such as Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), CNN, LSTM, GRU, and Adaptive Neuro-Fuzzy Inference System (ANFIS-FCM). The accuracy of the prediction models is assessed using various statistical criteria and visual comparisons. Results show that the proposed hybrid CNN-LSTM and CNN-GRU models in one-day-ahead NO2 concentration predictions yield the best results among all models with R2 accuracy of 0.9547.
PubDate: 2025-01-09
- A likelihood ratio test for circular multimodality
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Abstract: The modes of a statistical population are high frequency points around which most of the probability mass is accumulated. For the particular case of circular densities, we address the problem of testing if, given an observed sample of a random angle, the underlying circular distribution model is multimodal. Our work is motivated by the analysis of migration patterns of birds and the methodological proposal follows a novel approach based on likelihood ratio ideas, combined with critical bandwidths. Theoretical results support the behaviour of the test, whereas simulation examples show its finite sample performance.
PubDate: 2025-01-03
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