Sampling Distribution Research Articles

Curriculum adaptation method based on graph neural networks for universal domain adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge between domains without prior knowledge of the label spaces. Category shift and domain shift are two primary challenges in UniDA, which require the method not only to distinguish common “known” samples from private “unknown” samples but also to align feature distributions of the common samples. In previous works, private samples have been involved to some extent in process of distribution alignment, leading to negative transfer. Therefore, we propose a curriculum adaptation method based on graph neural networks (GNN) to alleviate this issue. Specifically, our paper introduces a score to measure the transferability of samples and a curriculum adaptation strategy which only utilizes the most transferable samples for adaptation in each episode. To prevent classifier from overfitting on source samples, we assist classifier with the score and curriculum learning to obtain reliable pseudo-labels for target common samples. Furthermore, our method utilizes a graph neural network to aggregate features from similar samples across domains. Extensive experiments on three benchmarks demonstrate the effectiveness of our proposed framework.

Microsphere lens array embedded microfluidic chip for SERS detection with simultaneous enhancement of sensitivity and stability

Surface enhanced Raman spectroscopy (SERS) utilizes the fingerprint features of molecular vibrations to identify and detect substances. However, in traditional single focus excitation scenarios, its signal collection efficiency of the objective is restricted. Furthermore, the uneven distribution of samples on the SERS substrate would result in poor signal stability, while the excitation power is limited to avoid sample damage. SERS detection system always requires precise adjustment of focal length and spot size, making it difficult for point-of-care testing applications. Here, we proposed a SERS microfluidic chip with barium titanate microspheres array (BTMA) embedded using vacuum self-assembled hot-pressing method for SERS detection with simultaneous enhancement of sensitivity and stability. Due to photonic nano-jets and directional antenna effects, high index microspheres are perfect micro-lens for effective light focusing and signal collecting. The BTMA can not only disperse excitation beam into an array of focal points covering the target uniformly with very low signal fluctuation, but enlarge the power threshold for higher signal intensity. We conducted a proof-of-principle experiment on chip for the detection of bacteria with immuno-magnetic tags and immuno-SERS tags. Together with magnetic and ultrasonic operations, the target bacteria in the flow were evenly congregated on the focal plane of BTMA. It demonstrated a limit of detection of 5 cells/mL, excellent signal reproducibility (error∼4.84%), and excellent position tolerance of 500 μm in X–Y plane (error∼5.375%). It can be seen that BTMA-SERS microfluidic chip can effectively solve the contradiction between sensitivity and stability in SERS detection.

Non-Parametric Estimation of the Renewal Function for Multidimensional Random Fields

This paper addresses the almost sure convergence and the asymptotic normality of an estimator of the multidimensional renewal function based on random fields. The estimator is based on a sequence of non-negative independent and identically distributed (i.i.d.) multidimensional random fields and is expressed as infinite sums of k-folds convolutions of the empirical distribution function. It is an extension of the work from the case of the two-dimensional random fields to the case of the d-dimensional random fields where d>2. This is established by the definition of a “strict order relation”. Concrete applications are given.

Gene flow throughout the evolutionary history of a colour polymorphic and generalist clownfish.

Even seemingly homogeneous on the surface, the oceans display high environmental heterogeneity across space and time. Indeed, different soft barriers structure the marine environment, which offers an appealing opportunity to study various evolutionary processes such as population differentiation and speciation. Here, we focus on Amphiprion clarkii (Actinopterygii; Perciformes), the most widespread of clownfishes that exhibits the highest colour polymorphism. Clownfishes can only disperse during a short pelagic larval phase before their sedentary adult lifestyle, which might limit connectivity among populations, thus facilitating speciation events. Consequently, the taxonomic status of A. clarkii has been under debate. We used whole-genome resequencing data of 67 A. clarkii specimens spread across the Indian and Pacific Oceans to characterize the species' population structure, demographic history and colour polymorphism. We found that A. clarkii spread from the Indo-Pacific Ocean to the Pacific and Indian Oceans following a stepping-stone dispersal and that gene flow was pervasive throughout its demographic history. Interestingly, colour patterns differed noticeably among the Indonesian populations and the two populations at the extreme of the sampling distribution (i.e. Maldives and New Caledonia), which exhibited more comparable colour patterns despite their geographic and genetic distances. Our study emphasizes how whole-genome studies can uncover the intricate evolutionary past of wide-ranging species with diverse phenotypes, shedding light on the complex nature of the species concept paradigm.

What's luck got to do with it? A generative model for examining the role of stochasticity in age-at-death, with implications for bioarchaeology.

The role of "luck" in determining individual exposure to health insults is a critical component of the processes that shape age-at-death distributions in mortality samples but is difficult to address using traditional bioarcheological analysis of skeletal materials. The present study introduces a computer simulation approach to modeling stochasticity's contribution to the mortality schedule of a simulated cohort. The present study employs an agent-based model of 15,100 individuals across a 120 year period to examine the predictive value of birth frailty on age-at-death when varying the likelihood of exposure to health insults. Birth frailty, when accounting for varying exposure likelihood scenarios, was found to account for 18.7% of the observed variation in individual age-at-death. Analysis stratified by exposure likelihood demonstrated that birth frailty alone explains 10.2%-12.1% of the variation observed across exposure likelihood scenarios, with the stochasticity associated with exposure to health insults (i.e., severity of health insult) and mortality likelihood driving the majority of variation observed. Stochasticity of stressor exposure and intrinsic stressor severity are underappreciated but powerful drivers of mortality in this simulation. This study demonstrates the potential value of simulation modeling for bioarchaeological research.

Chlorophyll a in lakes and streams of the United States (2005–2022)

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110 .

Local versus global environment: the suppression of star formation in the vicinity of galaxy clusters

ABSTRACT In order to examine where, how and why the quenching of star formation begins in the outskirts of galaxy clusters, we investigate the de-projected radial distribution of a large sample of quenched and star-forming galaxies (SFGs) out to 30R500 around clusters. We identify the SFG sample using radio continuum emission from the Low-Frequency Array Two-metre Sky Survey. We find that the SFG fraction starts to decrease from the field fraction as far out as 10R500, well outside the virial radius of the clusters. We investigate how the SFG fraction depends on both large-scale and local environments, using radial distance from a cluster to characterise the former, and distance from fifth nearest neighbour for the latter. The fraction of SFGs in high-density local environments is consistently lower than that found in low-density local environments, indicating that galaxies’ immediate surroundings have a significant impact on star formation. However, for high-mass galaxies – and low mass galaxies to a lesser extent – high-density local environments appear to act as a protective barrier for those SFGs that survived this pre-processing, shielding them from the external quenching mechanisms of the cluster outskirts. For those galaxies that are not in a dense local environment, the global environment causes the fraction of SFGs to decrease toward the cluster centre in a manner that is independent of galaxy mass. Thus, the fraction of SFGs depends on quite a complex interplay between the galaxies’ mass, their local environment, and their more global cluster-centric distance.

Type of pectin affects the functionality of potato protein-pectin conjugates in emulsions

This study investigated the effect of the molecular structure of pectin on conjugate formation and its functionality in plant protein-based emulsions. For this purpose, high-methoxylated, low-methoxylated, amidated citrus pectin and their mixtures with potato protein were vacuum-dried at defined process conditions. The change in free amino groups, colour and molecular weight distribution assessed conjugation. The side reaction, such as pectin degradation, was monitored by the degree of methoxylation, galacturonic acid content and intrinsic viscosity. In addition, FT-IR and hydrophobicity measurements were performed to determine the changes in chemical structure due to the occurring reactions. The ζ-potential, solubility and oil droplet size reflected the functionality of the conjugated samples.The results confirm that, apart from the conjugation of protein and pectin, where the most significant decrease in free amino groups was obtained with an increasing degree of methoxylation or presence of amide groups, undesirable degradation of pectin occurring, resulting in a broad molecular weight distribution of the conjugate samples. The conjugate formation was the highest with amidated pectin. The FT-IR measurements confirmed the formation of Maillard conjugates. The FT-IR spectra showed a decrease in the intensity signals of amide I and amide II and the formation of new C–N bond maxima. In addition, the conjugation caused the shift of the protein's isoelectric point to the acidic environment, resulting in higher solubility and improved emulsion stability at the isoelectric point of the protein.Regarding emulsion stability, the conjugates with amidated pectin performed best and achieved the narrowest oil droplet size distribution over a wider pH range. Emulsion-stabilising properties of conjugates with high- and low-methoxylated pectin differed only slightly. To better understand the underlying mechanisms for emulsion stabilisation, future research will focus on the interfacial properties of the conjugates as determined by interfacial shear rheology and pendant drop analysis.

X-ray study of the density distribution of FFF-printed samples with different fill patterns

Three-dimensional printing has a wide range of applications in science and technology. Fused filament fabrication (FFF) is a commonly used 3D printing technology, which is now being increasingly employed in radiation physics. In FFF, the internal structure of an object is primarily determined by its fill pattern and selected print modes. Therefore, this study aims to examine the interaction between X-rays and 3D-printed plastic samples with various infill patterns. The 3D-printed objects were produced using FFF with plastic and different infill patterns, including Rectilinear, Grid, Triangles, Stars, Honeycomb, Concentric, Archimedean Chords, Gyroid, and Hilbert Curve. Infill densities of 80% and 90% were utilized. Tomographic methods were applied to analyze the resulting samples. The study provides tomograms of the internal structure for each infill pattern. It was observed that Rectilinear and Grid patterns produced the most homogeneous samples. The findings of this study contribute to understanding of the propagation of X-rays through 3D-printed plastic samples with complex internal structures.

Multiobjective History Matching Using Machine Learning Proxies-Assisted Iterative Rejection Sampling

Summary The effectiveness of subsurface assessment for various field development scenarios relies on accurately quantifying uncertainties during production forecasts. This study proposes a new design-of-experiment (DoE)-based multiobjective history matching (HM) workflow using machine learning (ML) proxies-assisted iterative rejection sampling (IRS). The HM target is to improve the HM outcome of all the objectives simultaneously and attain a series of reliable simulation models for production forecasting after a predefined number of iterations. The IRS workflow takes full advantage of various proxies to efficiently explore the solution space via Monte Carlo (MC) sampling. The MC samples are iteratively rejected according to each objective function, and the new simulation designs for the next iteration are intentionally selected from the accepted MC samples to best preserve the subsurface uncertainty. The strengths of IRS over the other two optimizers commonly used in the oil and gas industry, genetic algorithm (GA) and particle swarm optimization (PSO), are validated through three applications—an analytic six-parameter three-objective minimization problem, a complex 120-parameter five-objective HM for Brugge field, and a 41-parameter three-objective HM study for an offshore asset in the Gulf of Mexico. By comparing the performance against GA and PSO, this study demonstrates that IRS is able to reduce the values of all the objectives rapidly and guarantees a better posterior distribution of simulation samples in the parameter space. Thus, IRS would yield a series of reliable simulation models with a close match to the observation data and preserve a high diversity of model parameters. These models can play an important role in modern reservoir management to facilitate the decision-making process. In addition, the IRS algorithm itself only costs 0.2–0.5% of the computational resource and 1.4–12.0% of the waiting time, according to the field study. To the best of our knowledge, the IRS workflow is the first one that integrates various types of ML proxies into the multiobjective HM process in the literature.

Socioeconomic disparities in risk perceptions and precautionary actions against COVID-19 among the working age population aged 18–59 in Japan: a cross-sectional study

IntroductionRisk perceptions and precautionary actions against COVID-19 have been reported to be generally insufficient globally, and differences by subpopulation group have been concerning, as a key driver to widening health...

Large-scale Probabilistic Forecasting of Consumer Engagement of CPG Products using Heterogeneous Web Data

Consumer Packaged Goods (CPG) play a pivotal role in customer-centric industries. Understanding the distinctive features of such products and how customers engage with them is essential for CPG manufacturers to create customer-winning products. In this study, we explore an innovative data-centric approach to forecast customer engagement for CPG products by monitoring their digital evolution, particularly in the Snacks category in the USA. Traditional methods for consumer analysis such as surveys, focus groups are time consuming, costly and ineffective with limited scope. They also lack a forecasting component, making it difficult for CPG companies to make forward-looking decisions. However, with the emergence of big data, we can leverage user generated public data from social media, web search, e-commerce platforms to estimate consumer engagement and make long-term forecasts. To achieve this, we propose a systematic approach to accumulate and prepare large datasets from heterogeneous web sources for CPG products. We then use state-of-the-art deep learning based time series forecasting models to efficiently train and predict consumer engagement for the next 12 months, benchmarking their computational efficiency and forecasting performance. Our findings indicate that the DeepAR model outperforms all other models, with the lowest NRMSE (=0.378), RMSE (=14.848) MSE (=220.457), MASE (=0.871), and sMAPE (=0.306) values. Furthermore, we demonstrate methods for computing single forecasting points and prediction intervals using the forecasted sample distribution from the probabilistic models. The proposed approach will provide CPG businesses with valuable insights to make informed decisions about product development, marketing strategies, and supply chain management.

Systematic Research and Application of a 5G Medical Unmanned Aerial Vehicle to Deliver COVID-19 Nucleic Acid Samples.

The purpose of this study was to investigate the feasibility and effectiveness of COVID-19 throat swab samples delivered by medical drones in epidemic prevention and control. This study was carried out in both southern and northern hospital districts of the Affiliated Hospital of Jiangnan University from May to October 2022. The main participants were the Affiliated Hospital of Jiangnan University and Zhejiang Antwork Technology Co., Ltd. We first constructed an urban medical unmanned aerial vehicle (UAV) delivery system and developed a UAV-specific storage box for COVID-19 samples. The UAV system was used to transport COVID-19 throat swab samples from the northern hospital district to the southern hospital district, and the following indexes were obtained: (1) flight time of COVID-19 samples delivered by UAV, (2) real-time temperature of COVID-19 nucleic acid samples during transportation, and (3) the time of distribution of COVID-19 nucleic acid samples by road traffic as measured using the Baidu Maps application, compared with the flight time of UAV. The COVID-19 sample delivery system for urban medical UAV mainly consists of intelligent logistics UAV, low-temperature COVID-19 throat swab sample storage box, unmanned logistics hub, and cloud operation control platform. The flight distance between the northern and southern districts of the Affiliated Hospital of Jiangnan University was 10 km, and the ground distance was 24 km. From May 11 to October 28, 2022, a total of 1,190 UAV flights occurred. The average flight time was 13 minutes, which was 40 to 70 minutes faster than the average road travel time required for manual delivery of COVID-19 throat swab samples. At different time points in the day, UAV delivery efficiency increased by 67.5% to 82%. The use of 5G with the Internet of Things and UAV technology to deliver nucleic acid samples has the characteristics of fast speed, being unaffected by ground traffic conditions, and the ability to ensure the safety of nucleic acid samples in the transportation process, which is worthy of further study.

A Novel image processing technique for weighted particle size distribution assessment

The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D 10, D 50 , D 90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

Monte Carlo Based Sampling Distribution of Annual Rate of Exceedance for Earthquake Insurance

ABSTRACT Seismic hazard expressed in terms of annual rate of exceedance and is used to calculate the earthquake insurance premium. Annual rate of exceedance is a complicated function of magnitudes, distances from site to earthquake sources and attenuation. Due to its complexity, determination of exact sampling distribution of earthquake insurance premium is not an easy task. This research proposes Monte Carlo simulation approach to determine the sampling distribution of earthquake insurance premium. Annual rate of exceedance was simulated first and then the insurance premium calculated based on simulation of annual rate of exceedance. The simulation involves quantifying synthetic catalogue similar to historical catalogue. Its simulation is conducted in order to construct annual rate of exceedance as an indicator of earthquake risk used in earthquake insurance. The simulation of 25 iteration and sample size of 100 shows that the sampling distribution of insurance premium is skewed to the right. The idea of using Monte Carlo simulation to study the sampling distribution of annual rate of exceedance is the originality of the study and the study contributes to the methodology of earthquake insurance.

A multi-domain adaptive neural machine translation method based on domain data balancer

Most methods for multi-domain adaptive neural machine translation (NMT) currently rely on mixing data from multiple domains in a single model to achieve multi-domain translation. However, this mixing can lead to imbalanced training data, causing the model to focus on training for the large-scale general domain while ignoring the scarce resources of specific domains, resulting in a decrease in translation performance. In this paper, we propose a multi-domain adaptive NMT method based on Domain Data Balancer (DDB) to address the problems of imbalanced data caused by simple fine-tuning. By adding DDB to the Transformer model, we adaptively learn the sampling distribution of each group of training data, replace the maximum likelihood estimation criterion with empirical risk minimization training, and introduce a reward-based iterative update of the bilevel optimizer based on reinforcement learning. Experimental results show that the proposed method improves the baseline model by an average of 1.55 and 0.14 BLEU (Bilingual Evaluation Understudy) scores respectively in English-German and Chinese-English multi-domain NMT.

Independent and combined associations of handgrip strength and walking speed with cognitive function in older adults: evidence from a national cross-sectional study

Objectives Although there have been studies on the association of handgrip strength or walking speed alone with cognitive abilities, few studies have determined the combined associations of handgrip strength and walking speed with cognitive function. Therefore we aimed to explore the independent and combined associations of handgrip strength and walking speed with cognitive function in Chinese older adults using a nationally representative sample. Method This cross-sectional study included 4,577 adults aged 60 and older. Handgrip strength was measured using a dynamometer and walking speed was assessed using a 2.5-meter walking test. Both handgrip strength and walking speed were organized into low, normal, and high tertiles according to the sample distribution. Cognitive function was measured using the Telephone Interview for Cognitive Status. Results Handgrip strength and walking speed were significantly associated with cognitive function. Participants with low handgrip strength or low walking speed separately had a higher rate of lower cognitive function (adjusted odds ratio (OR): 1.22 (95% CI: 1.04 − 1.44) for low handgrip strength; 1.54 (95% CI: 1.31 − 1.81) for low walking speed). Those with both low handgrip strength and low walking speed had an additively higher rate of lower cognitive function (adjusted OR: 1.72 (95% CI: 1.32 − 2.24)). Conclusion Having low handgrip strength or low walking speed is associated with a greater likelihood of lower cognitive function and vice versa. The concurrence of having low handgrip strength and low walking speed has an additive effect on cognitive function in older adults.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.