Significant Errors In Estimation Research Articles

Multiple aperture underwater imaging algorithm based on polarization information fusion

Underwater optical imaging is the key technology to explore the underwater mystery. However, due to the absorption and backscattering effects of the media in the underwater environment, the image acquired by the detector will be severely degraded. In order to obtain the effective underwater scene information, it is necessary to restore the acquired underwater image. The restoration technology based on differential polarization is one of the main methods of restoring the underwater images, which can suppress the background scattered light by the common-mode suppression between orthogonal polarization graphs, thus realizing the restoration of underwater image. However, the relevant research shows that the restoration effect of this method is general for the underwater non-uniform light field. The main reason is that the estimation errors of polarization degree and background scattering intensity under the condition of the non-uniform underwater light field are large. Out of the above problem, in this paper we present the multiple aperture underwater imaging technology of fused polarization information. The method uses the camera array to realize the large virtual aperture imaging system, thus obtaining the wide-angle light field information, and then to fuse the depth information of the scene to realize the accurate estimation of background scattering light intensity and polarization degree under the condition of underwater non-uniform light field. The estimated parameter value can better reflect the global characteristics of the scene. Through the imaging experiments on the targets with different polarization degrees in the turbid underwater environment, comparing with the current advanced restoration algorithm, the results show that the proposed method can effectively solve the problems of background scattering and polarization degree significant estimation error caused by non-uniform underwater light field, and obtain high-quality restoration results. Through the contrast imaging experiment of the target in the underwater environment with different turbidity concentrations, the results show that with the increase of turbidity concentration of the water, the image recovery effect of the method in this paper is gradually weakened. However, it still has a good restoration effect at a large concentration. At the same time, imaging experiments are conducted on targets in underwater environments with different sediment concentrations. The results show that the method proposed in this paper can also obtain a better restoration image in the turbid water environment containing sediment.

A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability.

Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this paper we present a novel data dependent multiscale model for hyperspectral unmixing accounting for spectral variability. The new method incorporates spatial contextual information to the abundances in extended linear mixing models by using a multiscale transform based on superpixels. The proposed method results in a fast algorithm that solves the abundance estimation problem only once in each scale during each iteration. Simulation results using synthetic and real images compare the performances, both in accuracy and execution time, of the proposed algorithm and other state-of-the-art solutions.

An Improved Topography-Coupled Kernel-Driven Model for Land Surface Anisotropic Reflectance

The semiempirical kernel-driven model is commonly used for global surface reflectance characterization because of its simplicity and underlying physical meaning. However, the current kernel-driven reflectance models assume that the terrain is flat and homogeneous, and can induce significant errors in the surface reflectance estimation and subsequent parameter retrievals over rugged terrain. In this study, an improved topography-coupled kernel-driven (TCKD) reflectance model with the correction of diffuse skylight effects was proposed based on the diffused-equivalent slope model (dESM) and RossThick-LiTransit (RTLT) kernel-driven model. The TCKD model’s accuracy and effectiveness were evaluated using surface reflectance simulated by the radiosity approach and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Against simulated data, the results show that the TCKD model can accurately capture the distortion of the reflectance shape and hemispherical distribution caused by the topographic effects. Compared to MODIS data, the TCKD model has an overall better performance than the RTLT model across different spatial scales and land cover types. When the mean slope is larger than 35° at the 500-m resolution, the TCKD model’s near-infrared (NIR) root-mean-square error (RMSE) and the regression slope of the fitting line are 0.037 and 0.752, respectively, whereas those of the RTLT model are 0.049 and 0.645. Neglecting the diffuse skylight in the TCKD model can also lead to great bias in the reflectance retrievals. When the mean slope is 31°, as the ratio of diffuse skylight varies from 0 to 1, the NIR RMSE of the TCKD model decreases from 0.012 to 0.005, whereas that increases from 0.012 to around 0.02 if the diffuse skylight effects are neglected. These preliminary results demonstrate that the TCKD model is capable of improving the fitting ability of the kernel-driven model over rugged terrain and provides potentials for better retrieving and interpreting land surface parameters such as land surface albedo in mountainous areas.

Thermal diffusivity measurement of insulating materials at high temperature with a four-layer (4L) method

This article presents a temperature-temperature thermal characterization method for the measurement of the thermal diffusivity of insulating materials at high temperature. This novel method, noted 4L, is a transient absolute measurement method based on the estimation of the transfer function at the center of a symmetrical stack composed of two specimen of an insulating material sandwiched between two conductive metallic plates. Two different direct 1D semi-analytical models were developed. The first one considers purely conductive opaque materials and the second one takes into account the coupling between radiative and conductive heat transfer modes for purely scattering semi-transparent materials. The first model was used to estimate the thermal diffusivity of two different opaque insulating materials (calcium silicate boards) up to 800 °C with accuracy better than 10%. The second model was used to estimate the thermal diffusivity of a semi-transparent insulating material (ceramic foam) up to 1000 °C with an accuracy of approximately 10%. The second model was used to identify significant estimation errors occurring if a purely conductive model is used for semi-transparent materials. The conducto-radiative model was also used to estimate an average value of the mean extinction coefficient of the semi-transparent material.

From Sun to Interplanetary Space: What is the Pathlength of Solar Energetic Particles?

Abstract Solar energetic particles (SEPs), accelerated during solar eruptions, propagate in turbulent solar wind before being observed with in situ instruments. In order to interpret their origin through comparison with remote sensing observations of the solar eruption, we thus must deconvolve the transport effects due to the turbulent magnetic fields from the SEP observations. Recent research suggests that the SEP propagation is guided by the turbulent meandering of the magnetic fieldlines across the mean magnetic field. However, the lengthening of the distance the SEPs travel, due to the fieldline meandering, has so far not been included in SEP event analysis. This omission can cause significant errors in estimation of the release times of SEPs at the Sun. We investigate the distance traveled by the SEPs by considering them to propagate along fieldlines that meander around closed magnetic islands that are inherent in turbulent plasma. We introduce a fieldline random walk model which takes into account the physical scales associated to the magnetic islands. Our method remedies the problem of the diffusion equation resulting in unrealistically short pathlengths, and the fractal dependence of the pathlength of random walk on the length of the random-walk step. We find that the pathlength from the Sun to 1 au can be below the nominal Parker spiral length for SEP events taking place at solar longitudes 45E to 60W, whereas the western and behind-the-limb particles can experience pathlengths longer than 2 au due to fieldline meandering.

Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP.

Robust Electromagnetic Pose Estimation for Robotic Applications

A wireless electromagnetic field-based sensor system is proposed, which enables the tracking of moving objects, e.g., drones. The gathered up to 6-degrees of freedom information is complementary to existing sensing principles, e.g., global positioning system (GPS) or vision-based systems. In addition, it can be used for stand-alone navigation or noninvasive localization of medical devices inside the human body. The sensor system is comprised of an exciter and a sensor. The exciter can be mounted on a moving robot and generates an electromagnetic field. The field is measured by the sensor, and subsequently, the pose of the exciter with respect to the sensors' pose is estimated. Conductive objects in the vicinity of the sensor alter the measured magnetic field due to the induced eddy currents. In general, unmanned aerial vehicles or wheeled robots mainly consist of conductive materials, which cause a significant estimation error. This article introduces an interference-aware electromagnetic near-field-based pose estimation approach. Specifically, the change in the magnetic field due to close conductive and ferromagnetic objects is modeled. Iterative numerical solutions of Maxwell's equations, based on, e.g., finite-element method, are avoided. Instead, an analytic expression of the change in the magnetic field due to present eddy currents is given. The advantages of the proposed concept for model-based low-complexity pose estimation concepts are shown using an extended Kalman filter. It is observed that the tracking performance using the introduced model outperforms the traditional model in eddy current scenarios significantly.

A numerical study on nonlinear surface wave evolution over viscoelastic mud

Accurate prediction of wave energy dissipation over mud is essential for understanding nearshore circulation, sediment transport processes, and design of engineering projects along muddy coasts. It is desirable to be able to simulate wave propagation over a wide range of mud behaviors as wave evolution is highly dependent on mud rheological characteristics. In this study, we develop a numerical model to simulate surface wave evolution over viscoelastic muds. This new wave-mud interaction model is comprised of a frequency-domain phase-resolving model for wave propagation that explicitly solves nonlinear wave-wave interactions, and a model for mud-induced surface wave dissipation and modulation. Model results show satisfactory agreement with laboratory measurements of wave height and attenuation rates over mud, and shows improvement over the model with a viscous mud mechanism. The model is then used to investigate the combined effect of mud viscoelasticity and nonlinear wave-wave interactions on surface wave evolution. Cnoidal and random wave simulations are conducted. In general, qualitative measures such as shape of cnoidal waves or pattern of variation in Hrms of random waves are dictated by direct mud-induced damping which due to resonance effects, has a substantially different structure over viscoelastic mud compared to viscous mud. Nonlinear interactions affect spectral shape and distribution of energy loss across the spectrum. Subharmonic interactions cause indirect damping of high frequencies but ameliorate damping of harmonics around mud’s resonance frequency. Therefore, neglecting mud elasticity can result in significant errors in estimation of bulk wave characteristics and spectral shape.

A Distortion Model of Laser Sheet for a Laser Line Scanner With Large Fan Angle

This article proposes an analytical method of computing distorted laser sheet of a laser line scanner and compensating it. In this article, a distortion model of laser sheet generated from a laser line scanner with large fan angle is derived from physical principles to achieve accurate depth perception even around its edges on which significant depth estimation errors occur in existing algorithms. From a laser beam incident obliquely on a contact surface of two cylindrical lenses, a curved laser sheet is expressed in terms of the nonzero incident angle by the laws of geometrical optics. From the mathematical model of the distorted laser sheet, the incident angle is estimated through an optimization technique and then its estimate is used for depth computation. It is shown through simulations and experiments that the proposed distortion model enables a proper compensation scheme and hence the distortion-compensated depth estimation errors are reduced all over the range of interest, specially much around the both side edges of the laser sheet. Quantitatively in comparison to the compensation-free method, the depth estimation error is improved from 225 to 28 mm on average and from 1781 to 187 mm for worst-case scenarios near edges of a laser sheet.

Using dose–response functions to improve calculations of the impact of anthropogenic noise

Abstract Estimating the number of animals impacted by a stressor typically involves combining a dose–response function with information about the distribution of animals and of the stressor. Regulators often prefer a single threshold to a full dose–response function, but much of the variability observed in the threshold at which different individuals respond to a stressor is an inherent characteristic of populations that needs to be taken into account to predict the effects of stressors. When selecting an exposure threshold, regulators need information on the proportion of the population that will be protected. Regulatory processes that calculate the number of animals impacted must draw from the dose–response function, the actual distribution of the animals, and a model mapping how the stressor intensity declines with distance from the source. Ignoring any of these factors can lead to significant errors in estimates of the area and numbers of animals affected. This paper focuses on behavioural responses of marine mammals to anthropogenic sound and demonstrates that a common approach of selecting the threshold at which half of the animals respond (RLp50) grossly underestimates the number of animals affected. We present an example, using a published dose–response function, where the number affected is underestimated by a factor of 280. Results would be similar for any stressor whose strength decreases following an inverse‐square function as it dilutes into the environment. This paper presents a method to use a dose–response function to derive a more accurate estimate of animals affected and to set a threshold (the Effective Response Level) that corrects the problem with the RLp50 estimate. Estimates of effects of stressors should include estimates of uncertainty, which can be used to adapt thresholds to different policy contexts and conservation problems.

Learned sketches for frequency estimation

The Count-Min sketch and its variations are widely used to solve the frequency estimation problem due to its sub-linear space cost. However, the collisions between high-frequency and low-frequency items introduce a significant estimation error. In this paper, we propose two learned sketches called the Learned Count-Min sketch and Learned Augmented sketch. We combine the machine learning methods with the traditional Count-Min sketch and Augmented sketch to improve the performance. We used a regression model trained from historical data to predict the frequencies and separate the high-frequency items and low-frequency items. The experimental results indicated that our learned sketches outperform the traditional Count-Min sketch and Augmented sketch. The learned sketches can provide a more accurate estimation with a more compact synopsis size.

Efficient localization of target in large scale farmland using generalized regression neural network

SummaryAlthough simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real‐time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range‐based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)‐based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN‐based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds.

Low complex direction of arrival estimation method based on adaptive filtering algorithm

Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their implementation is difficult in real-time applications. However, a DOA estimation method based on fixed step size least mean square (LMS) algorithm has overcome this deficiency but the suitable selection of step size is very difficult. A DOA estimation method based on the variable step size LMS algorithm is proposed in this article. In the proposed method, the step size is updated by using the estimated error signal. The spatial spectrum is obtained by the reciprocal of array pattern, where the peak values indicate the estimated DOAs of signals. The proposed method can provide better performance with low computational cost. The performance of proposed method is verified by the simulations and compared with some existing methods.

Performance analysis of unreliable manufacturing systems with uncertain reliability parameters estimated from production data

ABSTRACTSystem engineering methods for the performance evaluation of manufacturing systems require the estimation of input parameters, which is either based on real data or experts’ knowledge. In both cases, the input parameters are subjected to uncertainty. However, most of the systems engineering methods, which are based on analytical models assume that machine reliability parameters such as Mean Time to Failure and Mean Time to Repairs are precisely known. In order to overcome this limitation, this paper proposes an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine reliability estimates. The method enables to calculate the distribution of the output performance, given the distribution of the input parameters’ uncertainty. The evaluation procedure is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing transfer lines composed of unreliable machines and capacitated buffers. Experimental results obtained by using the method show that neglecting uncertainty in the input parameter estimates generates significant errors in the output performance measure estimation, thus making the subsequent system operation and reconfiguration decisions sub-performing. Finally, a real case study is presented to demonstrate the potential benefits of the proposed method for real industrial applications.

A time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm for indoor non-line-of-sight locationing

In complex indoor propagation environment, the non-line-of-sight error caused by various obstacles brings great error to node positioning. Choosing the appropriate signal transmission methods is important to improve node indoor positioning accuracy. In this research, ultra-wideband technology, as baseband with high theoretical positioning accuracy and real-time performance, is implemented to transmit indoor signals. The proposed fusion algorithm with ultra-wideband baseband takes advantages from both time difference of arrival and angle of arrival algorithms, combined through the steepest descent algorithm. The non-line-of-sight signal estimation error is iteratively eliminated to achieve effective positioning accuracy. The experimental results indicate that the novel time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm can largely improve node positioning accuracy and stability.

Local and Target Exploration of Conglomerate-Hosted Gold Deposits Using Machine Learning Algorithms: A Case Study of the Witwatersrand Gold Ores, South Africa

Determining the gold grade and facies type in areas with little geological information and sparse exploration samples is fraught with uncertainties and often results in high operational costs. Point-wise gold grade data are commonly used to guide exploration and resource estimation with the application of spatial interpolation techniques such as kriging. Within this environment of data scarcity, the application of kriging leads to significant grade estimation errors, as high nugget thresholds reduce the effectiveness of kriging, a good example being the gold deposits in the Witwatersrand Basin of South Africa. To reduce the impact of subjective grade interpolation and geological interpretation, as well as to exploit currently unused geological descriptions, we present a novel machine learning-based algorithm called GS-Pred. It combines both sedimentological and gold assay data for point-wise gold grade prediction and automated facies identification in a conglomerate-hosted gold deposit. For this application, GS-Pred requires an input database of sedimentological descriptions, spatial information and gold grades and makes predictions of gold grades at any point within the spatial coverage of the input database, provided that it has appropriate sedimentological descriptions. In essence, GS-Pred examines the spatial and non-spatial variability of metal grades and provides information of the estimated resource below the nugget threshold. This proposed algorithm has been validated on subsets of data on gold grade and sedimentological characteristics of conglomerates in the Witwatersrand Basin. Validation results suggest that GS-Pred is more accurate than current machine learning techniques and ordinary kriging. The clustering result shows that there are four or at most five facies which can be distinguished from the clustering results within the dataset, which maximises the contrast in the inter-cluster prediction behavior. These clusters have a good spatial correspondence with the known geology, and the method, combined with gold grade predictions, was able to identify probable mineralization patterns, thus assisting in target exploration. This novel machine learning algorithm is entirely data driven. We have shown its successful application in a complex geological setting as the Witwatersrand Basin.

Importance subsampling: improving power system planning under climate-based uncertainty

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation-based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios.

A Stochastic Method for Modelling the Geometry of a Single Fracture: Spatially Controlled Distributions of Aperture, Roughness and Anisotropy

We describe a simple but effective stochastic method to model the void structure of a single fracture in a form of voxel representation. A fracture void is delineated by two bounding wall surfaces that are separated by some distance (i.e. the local aperture) at each location on the medial surface that lies within the fracture void and serves as a model reference frame. The three surface height fields are generated based on four parameters (mean, standard deviation and two spatial correlation lengths) for each field and two parameters (coefficient and synergistic length) for the spatial correlation between the fracture walls. Testing of generated models demonstrates that not only are the model fracture apertures spatially correlated and characterized as a Gaussian field, but also the two fracture walls are closely correlated, with a similar shape and/or height. With respect to fracture apertures, three quantities, i.e. the mean aperture, roughness and anisotropy, can be derived from the fracture models to describe fracture morphology. The effect of model fractures on fluid flow is investigated in order to establish the relationship between fracture permeability and the three morphological quantities, revealing a way to avoid the significant estimation error associated with the use of the cubic law.

High speed RLC equivalent RC delay model for global VLSI interconnects

Current-mode signaling significantly is known for increasing the bandwidth of on-chip interconnects and reduces the overall propagation delay. In this paper feature of current mode interconnects is exploited for investigating the performance of RLC equivalent ReffCT mathematical delay model of interconnects. This is due to a simple RC interconnects model which results a significant error in delay estimation. Due to this equivalency the non ideal effect of inductive behavior at high frequencies and scaled technologies can be suppressed. The dominance of inductance effect is optimized by Simulative Sweep Analysis Techniques (SSAT). Accuracy is verified by analytical and SPICE simulation results. The performance of delay model is further estimated for voltage and current mode interconnects. When test results are estimated with voltage and current mode systems, it is observed that the equivalent model is superior to the traditional Elmore and Sakurai delay model.

Evaluation of 3B42V7 and IMERG daily-precipitation products for a very high-precipitation region in northwestern South America

The availability of water is critical in determining the distribution of species by favoring or limiting their development, and leading to the formation of different ecosystems. Thus, analyzing the trends and fluctuations of the precipitation is a key factor to understanding our planet's biodiversity. Different physical conditions exist in northwestern South America that cause extreme climate conditions, especially on the west coast of the continent. This region, known as Biogeographic Chocó, has high annual precipitation caused by interactions between the humid currents of the Pacific Ocean and the Andes Mountains. A limited network of hydro-meteorological stations are available in the region to monitor precipitation. Satellite precipitation products can provide valuable information in the absence of field stations, complementing the network of field stations in remote areas, and completing time series for stations that have stopped working. However, remote sensing data must be validated before being used. The goal of this study is thus to evaluate the accuracy of the products of the Tropical Rainfall Measuring Mission 3B42V7 and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), under very high precipitation conditions, and evaluate their strengths and shortcomings. These programs provide daily-precipitation information with a spatial resolution of 0.25° for the former and 0.1° for the latter. The validation was done by using a time series of daily-precipitation data obtained from 185 hydro-meteorological stations distributed over the Biogeographic Chocó. Different statistic metrics were used in the evaluation and comparison: error metrics (mean difference MD, relative mean difference RMD, and root mean square error RMSE), a correlation metric (Pearson correlation CP), contingency metrics (probability of detection POD, false-alarm ratio FAR, and critical success index CSI). We also evaluate the grid and areal scale. The results show that (i) the 3B42V7 and IMERG daily-precipitation products represent well the spatial and temporal distribution of mean daily precipitation over the Biographic Chocó, and both products are accurate for detecting precipitation events. (ii) Mean daily precipitation tends to be overestimated in areas with relative low precipitation and medium-to-high altitude whereas, on the contrary, mean daily precipitation tends to be underestimated in areas with very high precipitation and medium-to-low altitude. (iii) Finally, copious precipitation (i.e., an annual accumulated precipitation over 5000 mm, which is common for over 55% of the study area) strongly affects the accuracy of the satellite products, leading to significant errors in estimates of daily precipitation for some regions. This study constitutes one of the first exhaustive validation of the IMERG daily precipitation product over the Biogeographic Chocó, and the results provide important information about the potential for using this product in the study area and over regions with high precipitation.