Map Approach Research Articles

Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map

BackgroundSmall unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals.MethodsWe investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions.Review findingsWe found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review.ConclusionsOur systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images.

The analysis of the electronic customer relationship management system based on marketing performance and knowledge management of the company using the Fuzzy cognitive map approach

The analysis of the electronic customer relationship management system based on marketing performance and knowledge management of the company using the Fuzzy cognitive map approach

Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable saliency maps for RL policies. In this work, we propose an approach of Distillation with selective Input Gradient Regularization (DIGR) which uses policy distillation and input gradient regularization to produce new policies that achieve both high interpretability and computation efficiency in generating saliency maps. Our approach is also found to improve the robustness of RL policies to multiple adversarial attacks. We conduct experiments on three tasks, MiniGrid (Fetch Object), Atari (Breakout) and CARLA Autonomous Driving, to demonstrate the importance and effectiveness of our approach.

Unsupervised learning of interacting topological phases from experimental observables

Classifying topological phases of matter with strong interactions is a notoriously challenging task and has attracted considerable attention in recent years. In this paper, we propose an unsupervised machine learning approach that can classify a wide range of symmetry-protected interacting topological phases directly from the experimental observables and without a priori knowledge. We analytically show that Green’s functions, which can be derived from spectral functions that can be measured directly in an experiment, are suitable for serving as the input data for our learning proposal based on the diffusion map. As a concrete example, we consider a one-dimensional interacting topological insulators model and show that, through extensive numerical simulations, our diffusion map approach works as desired. In addition, we put forward a generic scheme to measure the spectral functions in ultracold atomic systems through momentum-resolved Raman spectroscopy. Our work circumvents the costly diagonalization of the system Hamiltonian, and provides a versatile protocol for the straightforward and autonomous identification of interacting topological phases from experimental observables in an unsupervised manner.

“Sponge Maps”: Using the Concept of Value by Area Maps for Avoiding the Area Size Bias in Choropleth Maps

A well-known problem with choropleth maps is the cognitively induced effect that larger regions are perceived as more dominant. Consequently, unsatisfactory detection rates for small areas can result, which becomes relevant when important spatial features or patterns are explored (e.g., regions with maximum values). One possible approach to avoiding the area size bias is the use of cartograms. While there is already some work on Equal Are Unit Maps, little or no research has been done on the possibility of applying the concept of Value By Area Maps to transform the reference area maps. One goal of this article is to introduce the concept of so-called “Sponge Maps”, which distort the base maps independently of thematic attributes, but depending on the need to show or emphasize certain areas of interest. The second goal of the article is to answer the overall research question whether Sponge Maps actually reduce the area size bias and improve the detectability of maximum value regions. A user study was able to verify the effectiveness of the Sponge Map approach in particular. However, it also became clear that not only the area size bias plays a role in the detection of important regions—dependencies on the absolute position (top-down bias), compactness or conspicuous shape (shape bias), the familiarity (awareness bias), the color intensity (darkness bias) and not least by the distortions in the Sponge Maps as such (distortion bias) are shown. Furthermore, special aspects of detecting minimum value regions are revealed (including the so-called inverse area size bias).

An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

Statistical properties related to angle variables in Hamiltonian map approach for one-dimensional tight-binding models with localization

Statistical properties related to angle variables in Hamiltonian map approach for one-dimensional tight-binding models with localization

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging the ToxCast library.

The thyroid stimulating hormone receptor (TSHR) is crucial in thyroid hormone production in humans, and dysregulation in TSHR activation can lead to adverse health effects such as hypothyroidism and Graves' disease. Further, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Hence, several such chemicals have been screened for their adverse physiological effects in human cell lines via high-throughput assays in the ToxCast project. The invaluable data generated by the ToxCast project has enabled the development of toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. Here, we systematically investigated the heterogeneity in structure-activity as well as structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing a structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Subsequently, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and TSHR antagonist datasets, we identified 3 chemical pairs as strong MOA-cliffs and 19 chemical pairs as weak MOA-cliffs. In conclusion, the insights from this systematic investigation of the TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterization of the chemical exposome.

Inverse Modeling to Constrain Composition of CO2-Rich Parental Melt of Kimberlite: Model Development and Application to the Majuagaa Dyke, Southern West Greenland.

Abstract A model is developed to test the hypothesis that kimberlites can form by low-degree melting of asthenospheric mantle followed by entrainment and assimilation of lithospheric mantle. The developed model uses inversion calculations based upon rare earth and compatible trace elements. For kimberlites (s.s.), an equation describing mass balance between a melt of unknown composition and a contaminant end-member of xenocrystic/assimilated material from the lithospheric mantle is inverted. This allows calculation of the mass fraction of xenocrystic minerals from the lithospheric mantle (olivine, orthopyroxene, clinopyroxene, garnet, ilmenite) entrained in the kimberlitic magma, as well as the source mineralogy and melt degree in the source region. The composition of the parental melt prior to interaction with the lithosphere is not assumed a priori but is calculated by the model. The CO2, H2O, K2O and P2O5 contents of the source are estimated assuming batch melting and the inversion models. The range and coupling of the model parameters are found using a non-linear most-squares inversion procedure, and the model space is visualised using a Self-Organising Map approach. Our earlier work supporting assimilation of xenocrystic opx is, however, not a precondition but provides a post-processing constraint, as well as the selection of a more likely set of solutions from the Self-Organising Map. The calculation is applied to a data set from the Majuagaa kimberlite dyke (southern West Greenland) including added whole rock analyses for CO2 and H2O. Major variations in whole rock compositions are related to flow differentiation of olivine macrocrysts. The textures of opx, cpx, gt and ilm megacrysts show evidence for reaction with the transporting melt and physical erosion in the kimberlitic mush. Using the bulk rocks in our inversion scheme results in a silico-carbonatite parental melt with major element concentrations consistent with experimental melts. The ol, opx, and cpx mass fractions in the source are not well-resolved by this calculation, but the proportion of gt in the source is comparatively well defined at 15–22 wt% and cpx is constrained to less than 14 wt%. The source assemblage required is 36–80 wt% ol, 2–49 wt% opx, 0–6 wt% cpx, and 15–19 wt% gt. This suggests a peridotitic rather than an eclogitic source. The inversion model gives an overall mass fraction of xenocrystic material in the Majuagaa kimberlite magma of 41–51 wt% The mass fractions of the xenocryst phases are as follows: 71–85 wt% ol, 0–13 wt% opx, 5 ± 1 wt% gt, and 10–14 wt% ilm. There is less than 3 wt% cpx in the xenocrystic and assimilated assemblage. These results agree with petrographic observations. Processing the model results using the Self-Organising Map clearly displays the extent and coupling within the statistically acceptable region of the model space and leads us to a preferred model of 49 wt% xenocrysts with a xenocryst assemblage of 71–76 wt% ol, 8–13 wt% opx, 4 wt% gt and 12 wt% ilm. A source with a REE pattern similar to that of primitive mantle is sufficient to form the parental melt and consistent with generation of the initial kimberlite melt in the convecting mantle. Calculated CO2 and H2O concentrations in the source of the Majuagaa kimberlite of 230–860 μg/g and 223–741 μg/g, respectively, are within the range of independent convecting mantle estimates. This is equivalent to <0.17 wt% magnesite and the H2O budget of the mantle source can be accommodated via storage in nominally anhydrous silicate phases. When applied to Majuagaa kimberlite, the inversions are consistent with a conceptually simple model of kimberlite formation: (1) low degree melting in carbonated asthenospheric peridotite, (2) melt extraction and concentration, and (3) entrainment and reaction with lithospheric mantle material.

Image encryption approach using improved chaotic system incorporated with differential evolution and genetic algorithm

Meta-heuristic techniques are widely used to solve different complex real-life problems in recent scenarios. In this work, traditional key-generation mechanisms have been combined with meta-heuristic techniques to derive new key generation models in chaos-based image cryptography. Initially, one among the key-strings has been generated using the logistic map approach that is known for its ability to produce effective chaotic performance. In this paper, the chaotic effect of the key-string generated by the logistic map has further been enhanced by the application of differential evolution (DE), a population-based meta-heuristic optimization algorithm. This improved key sequence is used to produce chaotic a map which has been applied to images for diffusion operation. The confusion operation on the other hand is performed by the transposition process using another key-string produced by the application of another meta-heuristic technique, namely, Genetic Algorithm (GA). After thorough experimental works, the results have been compared with the state-of-the-art and it is observed that the approach proposed here provides strong cryptographic behavior in terms of information entropy, key sensitivity, robustness against noise, and hereby stands against all kinds of statistical and differential attacks including brute force.

Mining CSF_tau associated genes with deep learning in AD patients

AbstractBackgroundTau protein abnormalities have been attracting more and more attention in Alzheimer’s disease (AD) pathophysiology. Tau spreads in a hierarchical pattern in the AD brain, likely by trans‐synaptic propagation between neurons. Discovering genetic risk factors associated with tau pathology could be key for improving our ability to predict and treat AD. In this study, we leveraged the non‐linear analytic approach of deep learning to identify genes that are associated with cerebrospinal fluid (CSF) total tau levels.MethodWe selected 384 subjects [118 normal control and 266 mild cognitive impairment (MCI)] from ADNI cohort. We designed a deep neuron network model that predicted CSF total tau levels using peripheral blood transcriptomic data. More specifically, we developed an occlusion map approach to rank 2,364 transcripts, which were initially selected from 49,386 transcripts by dimension reduction. The occlusion map aimed to rank the transcripts and identified the critical ones.ResultOur deep neural network successfully predicted CSF total tau levels with an accuracy of 67% and the occlusion map identified the critical genes for the prediction. The list included genes like TTL5, AKIRIN2, CPEB1, PTPN7, CIRBP and RPS23, which have been previously reported to be related to AD. For example, TTL5 relates to missorting of tau protein. AKIRIN2 is essential for the formation of the cerebral cortex. CPEB is found to be an interacting factor of amyloid‐precursor protein (APP). PTPN7 belongs to protein tyrosine phosphatases family, among which PTPN1& PTPN5 has been reported to tau pathology and amyloid pathology. CIRBP is reported to exert protective effects against neuronal amyloid toxicity. RPS23 regulates amyloid level and tau phosphorylation.ConclusionUncovering the genes that have critical association with tau protein abnormalities is critically important for improving our understanding of the drivers of neurodegeneration in prodromal AD. Our deep learning algorithm has shown promising results in identifying the critical genes for tau pathology. This study has demonstrated the effectiveness of deep learning in genetic analysis for AD research.1. Bahureksa, L., et al. (2017)2. Wilcockson, T.D.W., et al. (2019)3. Tomoeda, C. K. (2001)4. Feldman, H. H., et al. (2008)5. Winblad, B., et al. (2016)6. Thomann, A. E., et al. (2018)

In-situ monitoring system for weld geometry of laser welding based on multi-task convolutional neural network model

This paper presents a low-cost, robust, in-situ monitoring system for weld geometry that can achieve multi-task prediction. First, the system uses a low-cost CCD camera to monitor the melt pool in the laser keyhole welding process. Then, the proposed novel multi-task convolutional neural network (Multi-task CNN) model is used to simultaneously complete the two prediction tasks of weld depth and width. Furthermore, the learning process of the Multi-task CNN model is explored using a visual feature map approach and the robustness of the model is demonstrated. Compared with Support Vector Machine, K-Nearest Neighbor, Bayesian Ridge, Decision Tree, the proposed Multi-task CNN model has the highest prediction accuracy. The model predicts a mean absolute percentage error (MAPE, relative to ground truth) of 3.0% and 1.9% for weld depth and width. The in-situ monitoring results show that the system can achieve accurate predictions, and the average time-consuming of the system is 23.35 ms.

Improved Structural Local Thermal Energy Planning Based on Prosumer Profile: Part B

Distributed energy systems (DES) are currently at the forefront of the energy transition. Their placement brings production closer to the demand side of urban and sub-urban environments, making optimal design a necessity. However, the complexity of accurately addressing the energy demands via DES has received increasing research attention. This is mainly due to the impact they have on the energy transition’s socioeconomic aspect, as these systems are far from viable in most cases, especially when cutting-edge renewable technologies are involved. The current study aims to provide a practical and non-repetitive approach to DES design, explicitly referring to thermal distributed supply systems (TDESS). The authors present the last two of their three-layer Hierarchically Dependent Layering Methodology (HDLM) approach in designing a thermal local energy community (TLEC) from the ground up. The 2nd layer is the superstructure design of the TLEC, where a map approach is introduced and explores several combinations of the selected equipment, how they will interact to meet the heating and cooling loads and how they will form the superstructure. The 3rd is the economic assessment of the proposed scenario. The study results indicate relative ease of use of the model, as a non a priori approach is needed. Additionally, the proposed solution is economically viable as the respective performance indicators suggest.

Health risk assessment of PAHs in fruit juice samples marketed in city of Tehran, Iran.

The objective of the present study was to investigate the level of 16 PAHs in fruit juice samples (orange, apple, peach, pineapple, and mango) with three different packages (PET bottle, Tetra Pak, and canned packaging) by using MSPE/GC-MS (magnetic solid-phase extraction and gas chromatography-mass spectrometry) method. In this method limit of detection (LOD), and limit of quantitation (LOQ), and recovery were 0.030-0.280 μg/L, 0.090-0.840 μg/L, and 94.8-102%, respectively. Our results showed the median of total PAHs and PAH4 (in all samples) were 7.67 ± 3.19 and 0.370 ± 0.160 μg/L, respectively. The median of BaP in samples was )0.060 ± 0.030 μg/L( lower than the standard level (0.200 μg/L in drinking water) of US Environmental Protection Agency (USEPA). Also, our results showed that pineapple juice had a maximum median of total PAHs of 12.4 ± 4.84 μg/L and mango juice had a minimum median of total PAHs of 5.17 ± 1.24 μg/L. Additionally, canned packaging had a maximum average total PAHs of 10.6 ± 5.22 μg/L and PET bottles had a minimum average total PAH of 5.25 ± 2.03 μg/L. A heat map approach was also used to cluster samples. The Monte Carlo results indicated that the estimated daily intake (EDI) rank order was Na > B(g)P > Ch > I(cd)P > B(b)F > Ph > B(k)F > F > Ace > Fl > B(a)P > B(a)A > P >A. The Monte Carlo simulation (MCS) results showed the incremental lifetime cancer risk (ILCR) at the 95th percentiles for adults and children was 4.91 × 10-7 and 9.12 × 10-7, respectively. It is concluded that the concentration of PAHs compounds in Iranian fruit juices is lower than the existing standards, and in terms of the risk of carcinogenesis, it does not threaten the human health (< 10-6).

Some Formal Implications of Deletion Saltation

Abstract: Two classes of saltation alternations (phonological derived-environment effects) are formally distinct under Harmonic Grammar (HG), but formally equivalent under Optimality Theory (OT, with or without local constraint conjunction). The familiar feature-scale saltation pattern cannot be modeled in HG without additional formal devices (White 2013), but deletion saltation arises as a simple gang effect under classic Correspondence Theory faithfulness constraints. If future work finds corresponding empirical differences between the two saltation types (e.g., in learnability), this would support weighted-constraint models such as HG over ranked-constraint models such as OT, and in addition would be evidence against MAX and DEP constraints on privative features and against the *MAP approach to faithfulness.

Path Planning for Autonomous Systems Design: A Focus Genetic Algorithm for Complex Environments

Abstract Path planning has been a hot research topic in robotics and is a vital functionality for autonomous systems. As the time complexity of traditional path planning algorithms grows rapidly with the complexity of the problem, evolutionary algorithms are widely applied for their near-optimal solutions. However, evolutionary algorithms can be trapped in a local optimum or converge to infeasible solutions, especially for large search spaces. As the problem scale increases, evolutionary algorithms often cannot find feasible solutions with random exploration, making it extremely challenging to solve long-range path-planning problems in environments with obstacles of various shapes and sizes. For long-range path planning of an autonomous ship, the current downsampling map approach may result in the disappearance of rivers and make the problem unsolvable. This paper introduces a novel area-based collision assessment method for genetic algorithm (GA) that can always converge to feasible solutions with various waypoints in large-scale and obstacle-filled environments. Waypoint-based crossover and mutation operators are developed to allow GA to modify the length of the solution during planning. To avoid the premature problem of GA, the mutation process is replaced by a self-improving process to let the algorithm focus the operations on any potential solutions before discarding them in the selection process. The case studies show that the proposed GA-focus algorithm converges faster than RRT* and can be applied to various large-scale and challenging problems filled with obstacles of different shapes and sizes, and find high-quality solutions.

LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping

Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the limitations of onboard computation and memory resources needed for autonomous operation. In this work, we present LOCUS 2.0, a robust and computationally-efficient \lidar odometry system for real-time underground 3D mapping. LOCUS 2.0 includes a novel normals-based \morrell{Generalized Iterative Closest Point (GICP)} formulation that reduces the computation time of point cloud alignment, an adaptive voxel grid filter that maintains the desired computation load regardless of the environment's geometry, and a sliding-window map approach that bounds the memory consumption. The proposed approach is shown to be suitable to be deployed on heterogeneous robotic platforms involved in large-scale explorations under severe computation and memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR team's entry in the DARPA Subterranean Challenge, across various underground scenarios. We release LOCUS 2.0 as an open-source library and also release a \lidar-based odometry dataset in challenging and large-scale underground environments. The dataset features legged and wheeled platforms in multiple environments including fog, dust, darkness, and geometrically degenerate surroundings with a total of $11~h$ of operations and $16~km$ of distance traveled.

Nonprehensile manipulation of a stick using impulsive forces

The problem of nonprehensile manipulation of a stick in three-dimensional space using intermittent impulsive forces is considered. The objective is to juggle the stick between a sequence of configurations that are rotationally symmetric about the vertical axis. The dynamics of the stick is described by five generalized coordinates and three control inputs. Between two consecutive configurations where impulsive inputs are applied, the dynamics is conveniently represented by a Poincaré map in the reference frame of the juggler. Stabilization of the orbit associated with a desired juggling motion is accomplished by stabilizing a fixed point on the Poincaré map. The Impulse Controlled Poincaré Map approach is used to stabilize the orbit, and numerical simulations are used to demonstrate convergence to the desired juggling motion from an arbitrary initial configuration. In the limiting case, where consecutive rotationally symmetric configurations are chosen arbitrarily close, it is shown that the dynamics reduces to that of steady precession of the stick on a hoop.

A new lens to the understanding and reduction of household food waste: A fuzzy cognitive map approach

Food waste generated at the household level is known to be the main contributor to total food waste, particularly in developed regions. Reducing household food waste (HFW), however, is an extremely compelling task as there are many complex and interacting factors behind the HFW behavior. This study aims to address the factors behind the complexity by applying the Fuzzy Cognitive Map (FCM) method. FCM represents a new approach that enables the use of multiple resources, consideration of outnumbered factors, handling of linguistic ambiguities, and scenario analysis. Through FCM, this study aims to develop a more complete model of the complex HFW drivers' system and identify the most influential HFW drivers addressing which is key while designing HFW-reducing interventions. The current study employs a three-stage methodology that utilizes content analysis of scholarly published academic articles and exploits expert opinion to construct an FCM. In the final stage, through scenario analysis, the study tests and reports the effects of each HFW driver and evaluates them based on their potential to reduce HFW. The findings of this research reveal that system concepts A12 (fail to consume what is in the fridge), A2 (excessive purchasing), and A9 (cooking and serving too much) are the most influential practices concerning HFW. While the study suggests innovative approaches that would enable people not to give up their normality to cope with A12 , tackling A9 requires challenging the normality, and addressing A2 requires changing food store-related infrastructural elements. Moreover, the study draws attention to the concept C2 (food safety and health concerns) due to its potential to be a disincentive to FW reduction efforts as well as to the concept G1 (lack of knowledge/skill/awareness) which requires special attention to maximize its potential. Finally, the paper offers specific recommendations to practitioners and policymakers and provides future research directions.

Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts

ABSTRACT This study evaluates the performance of an enhanced Kohonen self-organizing map (eKSOM)-based bias-correction technique and compares the results with a copula-based bias-correction approach and the traditional quantile mapping (QM) to correct the daily multi-model ensemble short- to medium-range rainfall forecast products of the India Meteorological Department (IMD-MME) in the Hirakud Reservoir catchment in India. The copula and eKSOM outperformed the raw IMD-MME and QM rainfall across all lead times. While both the copula and eKSOM-based bias-corrected forecasts could satisfactorily provide the temporal pattern of the observed rainfall, the corresponding eKSOM-based forecasts presented better skills in capturing seasonality in the observed rainfall. The streamflow at one- to five-day lead times are simulated by forcing a suitable hydrological model, MIKE11 NAM (Nedbør Afstrømnings Model)-HD (Hydrodynamic) with the raw and bias-corrected rainfall inputs. The overall performance evaluation reveals significant improvement in both the rainfall and streamflow forecasts by copula and eKSOM, with the latter performing most accurately at higher lead times.