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Selection: with tag artificial-neural-networks [28 articles] 


What can machine learning do? Workforce implications

Science, Vol. 358, No. 6370. (22 December 2017), pp. 1530-1534,


Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no ...


Mapping indicators of female welfare at high spatial resolution



Improved understanding of geographic variation and inequity in health status, wealth, and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national scales conceal important inequities, with the rural poor often least well represented. High-resolution data on key social and health indicators are fundamental for targeting limited resources, especially where development funding has recently come under increased pressure. Globally, around 80% of countries regularly produce sex-disaggregated statistics at a ...


  1. Alegana, V.A., Atkinson, P.M., Pezzulo, C., Sorichetta, A., Weiss, D., Bird, T., ErbachSchoenberg, E., Tatem, A.J., 2015. Fine resolution mapping of population age-structures for health and development applications. Journal of The Royal Society Interface 12 (105), 20150073+. .
  2. Banerjee, S., Gelfand, A.E., Polasek, W., 2000. Geostatistical modelling for spatial interaction data with application to postal service performance. Journal of statistical planning and inference 90(1), 87-105. .

Exploring the high-resolution mapping of gender-disaggregated development indicators

Journal of The Royal Society Interface, Vol. 14, No. 129. (05 April 2017), 20160825,


Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential ...


Supplementary Information from Exploring the high-resolution mapping of gender disaggregated development indicators



[Excerpt: Datasets] The Demographic and Health Surveys (DHS) is a program of national household surveys implemented across a large number of LMICs. The DHS Program collects and analyses data on population demographic and health characteristics through more than 300 surveys in over 90 countries. The gender-disaggregated data we investigated in this report come from DHS datasets. [\n] [...] [Models specification] [::Bayesian model specification] The Gaussian Function (GF) in INLA is represented as a Gaussian Markov Random Function (GMRF). Computations in INLA are carried out using the GMRF by approximating a ...


  1. Alegana, V.A., Atkinson, P.M., Pezzulo, C., Sorichetta, A., Weiss, D., Bird, T., ErbachSchoenberg, E., Tatem, A.J., 2015. Fine resolution mapping of population age-structures for health and development applications. Journal of The Royal Society Interface 12 (105), 20150073+. .
  2. Bosco, C., de Rigo, D., Dijkstra, T.A., Sander, G., Wasowski, J., 2013. Multi-scale robust modelling of landslide susceptibility: regional rapid assessment and catchment robust fuzzy ensemble. IFIP Advances in Information and Communication Technology

Overcoming catastrophic forgetting in neural networks

Proceedings of the National Academy of Sciences, Vol. 114, No. 13. (28 March 2017), pp. 3521-3526,


[Significance] Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems ...


Robust modelling of the impacts of climate change on the habitat suitability of forest tree species

Keywords: abies-alba   array-of-factors   artificial-neural-networks   bioclimatic-predictors   change-factor   climate-change   data-uncertainty   diversity   environmental-modelling   europe   extrapolation-uncertainty   featured-publication   forest-resources   free-scientific-knowledge   free-scientific-software   free-software   fuzzy   gdal   genetic-diversity   geospatial   geospatial-semantic-array-programming   gnu-bash   gnu-linux   gnu-octave   habitat-suitability   integration-techniques   mastrave-modelling-library   maximum-habitat-suitability   modelling-uncertainty   multiplicity   peseta-series   python   regional-climate-models   relative-distance-similarity   robust-modelling   semantic-array-programming   semantic-constraints   semantics   spatial-disaggregation   sres-a1b   supervised-training   unsupervised-training  


[::] In Europe, forests play a strategic multifunctional role, serving economic, social and environmental purposes. However, their complex interaction with climate change is not yet well understood. [::] The JRC PESETA project series proposes a consistent multi-sectoral assessment of the impacts of climate change in Europe. [::] Within the PESETA II project, a robust methodology is introduced for modelling the habitat suitability of forest tree species (2071-2100 time horizon). [::] Abies alba (the silver fir) is selected as case study: a main European tree ...


  1. European Commission, 2013. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - A new EU forest strategy: for forests and the forest based sector. No. COM(2013) 659 final. Communication from the Commission to the Council and the European Parliament. , INRMM-MiD:12642065 .
  2. European Commission, 2013. Commission staff working document accompanying the document: Communication from the commission to

Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland

Global Ecology and Biogeography, Vol. 11, No. 6. (November 2002), pp. 453-462,


[Aim] Climate change has the potential to have significant impacts on the distribution of species and on the composition of habitats. This paper identifies the potential changes in the future distribution of species under the UKCIP98 climate change scenarios, in order that such changes can be taken into account in conservation management. [Location] The model was applied to Britain and Ireland. [Methods] A model based on an artificial neural network was used to predict the changing bioclimate envelopes of species in Britain and ...


A radiative transfer model-based method for the estimation of grassland aboveground biomass

International Journal of Applied Earth Observation and Geoinformation, Vol. 54 (February 2017), pp. 159-168,


[Highlights] [::] The PROSAILH radiative transfer model was presented to estimate grassland AGB. [::] The ill-posed inversion problem was alleviated by using the ecological criteria. [::] Multi-source satellite products were used to filter the unrealistic combinations of retrieved free parameters. [::] Three empirical methods were also used to estimate the grassland AGB. [Abstract] This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side ...


Web distributed computing for evolutionary training of artificial neural networks

In Proceedings of the International Conference on Information Technologies (InfoTech-2016) (September 2016), pp. 210-216


Evolutionary algorithms (EAs) are widely used in artificial neural networks training. EAs are computationally interesting because it is possible ot separate the problem solving in smaller pieces and to calculate these smaller pieces on different machines (distributed computing). Distributed computing platforms are well established and the most popular is BOINC, created in Berkeley. The problem in distributed computing platforms is the heterogeneity of the computational environment. The best way for solving heterogeneity is by using well established technology such as AJAX. ...


  1. McCulloch, W., Pitts, W., 1943. A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5 (4): 115–133. .
  2. Zissis, D., 2015. A cloud based architecture capable of perceiving and predicting multiple vessel behaviour, Applied Soft Computing 35.
  3. Forrest, M. D., 2015. Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster, BMC Neuroscience 16, 27.

Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data

Scientific Reports, Vol. 3 (13 November 2013),


Unplanned fire is a major control on the nature of terrestrial ecosystems and causes substantial losses of life and property. Given the substantial influence of climatic conditions on fire incidence, climate change is expected to substantially change fire regimes in many parts of the world. We wished to determine whether it was possible to develop a deep neural network process for accurately estimating continental fire incidence from publicly available climate data. We show that deep recurrent Elman neural network was the ...


Validation of species–climate impact models under climate change

Global Change Biology, Vol. 11, No. 9. (1 September 2005), pp. 1504-1513,


Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive ...


Does the interpolation accuracy of species distribution models come at the expense of transferability?

Ecography, Vol. 35, No. 3. (March 2012), pp. 276-288,


Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the ...


Nonlinear principal component analysis: neural network models and applications



Nonlinear principal component analysis (NLPCA) as a nonlinear generalisation of standard principal component analysis (PCA) means to generalise the principal components from straight lines to curves. This chapter aims to provide an extensive description of the autoassociative neural network approach for NLPCA. Several network architectures will be discussed including the hierarchical, the circular, and the inverse model with special emphasis to missing data. Results are shown from applications in the field of molecular biology. This includes metabolite data analysis of a ...


Neural Turing machines

(10 Dec 2014)


We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples. ...

Visual summary


Neuro-fuzzy and neural network systems for air quality control

Atmospheric Environment, Vol. 43, No. 31. (October 2009), pp. 4811-4821,


In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical ...


Assessment of soil erosion vulnerability in western Europe and potential impact on crop productivity due to loss of soil depth using the ImpelERO model

Agriculture, Ecosystems & Environment, Vol. 81, No. 3. (November 2000), pp. 179-190,


Soil erosion continues to be a major concern for the development of sustainable agricultural management systems. Sustainability modelling analysis for soil erosion must include not only vulnerability prediction but also address impact and response assessment, in an integrated way. This paper focuses on the impact of soil erosion on crop productivity and the accommodation of agricultural use and management practices to soil protection. From the Andalucia region in Spain, soil/slope, climate and crop/management information was used to further develop an expert-system/neural-network ...


Floods in a changing climate



"Flood risk management is presented in this book as a framework for identifying, assessing and prioritizing climate-related risks and developing appropriate adaptation responses. Rigorous assessment is employed to determine the available probabilistic and fuzzy set-based analytic tools, when each is appropriate and how to apply them to practical problems. Academic researchers in the fields of hydrology, climate change, environmental science and policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and environmental economics, will find ...


Mapping land cover from detailed aerial photography data using textural and neural network analysis

International Journal of Remote Sensing, Vol. 28, No. 7. (1 April 2007), pp. 1625-1642,


Automated mapping of land cover using black and white aerial photographs, as an alternative method to traditional photo?interpretation, requires using methods other than spectral analysis classification. To this end, textural measurements have been shown to be useful indicators of land cover. In this work, a neural network model is proposed and tested to map historical land use/land cover (LUC) from very detailed panchromatic aerial photographs (5 m resolution) using textural measurements. The method is used to identify different land use and management ...


(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 3

(February 2014)
Keywords: apennines   aphid   aphis-pomi   apl   apmv-transmission   approximate-dynamic-programming   apriona-germari   aqua-modis   aquatic-invertebrate-shredders   aradus-cinnamommeus   araucaria-angustifolia   araucaria-araucana   araucaria-bidwillii   araucaria-heterophylla   araucaria-montana   araucaria-spp   arboriculture   arbutus-canariensis   arbutus-menziesii   arbutus-spp   arbutus-unedo   arctic-region   arctostaphylos-uva-ursi   ardity   argania-spinosa   argentina   argive-plain   arid-climate   arid-region   aridity   arion-lusitanicus   aristolochia-arborea   armillaria-spp   array-atomic-variables   array-of-agents   array-of-factors   array-of-sectors   array-of-users   array-programming   arsenic   arthropods   artic-region   artic-sea-ice   artificial-intelligence   artificial-neural-network   artificial-neural-networks   artocarpus-altilis   artocarpus-heterophyllus   arvicola-spp   arxiv   asia   aspidosperma-cruentum   aspidosperma-myristicifolium   asplenium-spp   assessment   associated-microorganisms   association-genetics   associations   asteraceae   asynchronous-change   atmosphere   atmospheric-circulation   atriplex-halimus   atriplex-nummularia   atta-cephalotes   auc   australia   austria   austrocedrus-chilensis   authorship   autoecology   automatic-knowledge-generation   automatic-knowledge-mapping   automation   automation-irony   autonomic-computing   autoregressive-model   avicennia-germinans   avifauna   awk   azadirachta-indica   azerbaijan   azolla-spp   bacillus-thuringiensis   back-propagation-networks   bacteria   bacterial-canker   bacterial-diseases   bacterial-wood-degradation   bactris-gasipaes   bactrocera-invadens   bactrocera-oleae   baikiaea-plurijuga   balanites-aegyptiaca   balkan-peninsula   balkan-region   balkans   bangladesh   banksia-grandis   inrmm-list-of-tags  


List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( ). ...


Reservoir-system simulation and optimization techniques

Stochastic Environmental Research and Risk Assessment In Stochastic Environmental Research and Risk Assessment, Vol. 27, No. 7. (2013), pp. 1751-1772,


Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most ...


Expert system & knowledge engineering in Wikipedia

In Compilation (September 2012)
edited by Mohsen Kahani


Compilation of topics published in Wikipedia on Expert System and Knowledge Engineering: Expert system; Knowledge representation and reasoning, Reasoning system; Forward chaining; Rete algorithm; Backward chaining; Backward induction; Production system; Production Rule Representation; Inference engine; Fuzzy logic; Fuzzy control system; Artificial neural network; Types of artificial neural networks; Feedforward neural network; Self-organizing map; Hybrid intelligent system; Neuro-fuzzy; Genetic fuzzy systems; Knowledge engineering; Knowledge retrieval; Knowledge acquisition; Knowledge management; Data warehouse; Extract, transform, load; Star schema; Snowflake schema; Data mining; Cross Industry Standard Process for Data Mining; Statistical classification; Cluster analysis; Association rule learning; Sequence mining; Anomaly detection. ...


Coupling fuzzy modeling and neural networks for river flood prediction

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol. 35, No. 3. (August 2005), pp. 382-390,


Over the last decade, neural network-based flood forecast systems have been increasingly used in hydrological research. Usually, input data of the network are composed by past measurements of flows and rainfalls, without providing a description of the saturation state of the basin, which, in contrast, plays a key role in the rainfall-runoff process. This paper couples neural networks and fuzzy logic in order to enrich the description of the basin saturation state for flood forecasting purposes. The basin state is assessed ...


Multi-scale robust modelling of landslide susceptibility: regional rapid assessment and catchment robust fuzzy ensemble

IFIP Advances in Information and Communication Technology, Vol. 413 (2013), pp. 321-335,


Landslide susceptibility assessment is a fundamental component of effective landslide prevention. One of the main challenges in landslides forecasting is the assessment of spatial distribution of landslide susceptibility. Despite the many different approaches, landslide susceptibility assessment still remains a challenge. A semi-quantitative method is proposed combining heuristic, deterministic and probabilistic approaches for a robust catchment scale assessment. A fuzzy ensemble model has been exploited for aggregating an array of different susceptibility zonation maps. Each susceptibility zonation has been obtained by applying ...


AIGIS-based methodology for natural terrain landslide susceptibilitymapping in Hong Kong

Episodes, Vol. 24, No. 3. (2001), pp. 150-158


This paper presents a novel application of Artificial Intelligence (AI) and Geographic Information System (GIS) to the mapping of natural terrain landslide susceptibility in Hong Kong. The method is applied to the central part of Lantau Island as a pilot study. First, we discuss the key technical concepts of AI and GIS, the advantages of their integrated application, and the growing importance of remote sensing data. We then describe the working procedure including the construction of the GIS database, pre-treatment of ...


Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

Water Resources Management In Water Resources Management, Vol. 24, No. 6. (2010), pp. 1107-1138,


This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, ...


Neuro-Dynamic Programming for the efficient integrated water resources management

In Modelling and control for participatory planning and managing water systems (30 September 2004)


Design of policies within the integrated water resources management (IWRM) paradigm demands to take into account multiple objectives such as flood protection, water supply for hydropower generation, irrigation and urban use, imposition of minimum environmental flows, etc.. Finding efficient water management policies is a demanding task and Stochastic Dynamic Programming (SDP) can provide an effective solution methodology. The efficiency boost is even more pronounced when the scale of integration of the water management problem spans networks of water resources (i.e. sets of interconnected reservoirs, or wide-area ...


A selective improvement technique for fastening neuro-dynamic programming in water resources network management

IFAC-PapersOnLine In Proceedings of the 16th IFAC World Congress, Vol. 38, No. 1. (04 July 2005), pp. 7-12,
edited by Pavel Zítek


An approach to the integrated water resources management based on Neuro-Dynamic Programming (NDP) with an improved technique for fastening its Artificial Neural Network (ANN) training phase will be presented. When dealing with networks of water resources, Stochastic Dynamic Programming provides an effective solution methodology but suffers from the so-called "curse of dimensionality", that rapidly leads to the problem intractability. NDP can sensibly mitigate this drawback by approximating the solution with ANNs. However in the real world applications NDP shows to be ...


  1. Archibald, T.W., McKinnon, K.I.M., Thomas, L.C., 1997. An aggregate stochastic dynamic programming model of multireservoir systems. Water Resour. Res. 33, 333-340.
  2. Bellman, R.E., Dreyfus, S.E., 1959. Functional approximations and dynamic programming. Mathematical Tables and Other Aids to Computation 13, 247-251.
  3. Bellman, R.E., Kabala, R., Kotkin, B., 1963. Polynomial approximation – a new computational technique in dynamic programming. Mathematical Tables and Other Aids to Computation 17, 155–161.
  4. Bertsekas, D.P.,

Neuro-dynamic programming for the efficient management of reservoir networks

In Proceedings of MODSIM 2001, International Congress on Modelling and Simulation, Vol. 4 (December 2001), pp. 1949-1954,


[:Significance (Ed.)] This article introduced in 2001 one of the very first successful applications of advanced machine learning techniques to solve complex, multicriteria management problems in water resources dealing with networks of water reservoirs. It applied approximate dynamic programming (here, neuro-dynamic programming - whose approximation of stochastic dynamic programming relies on artificial neural networks) to the integrated water resources management. The methodology is general enough to be potentially useful in other problems of integrated natural resources modelling and management (INRMM). [::Machine learning in ...


  1. Bellman, R.E., Dreyfus, S.E., 1959. Functional approximations and dynamic programming. Mathematical Tables and Other Aids to Computation, 13, pp. 247–251.
  2. Bertsekas, D.P., Tsitsiklis, J.N., 1996. Neuro-Dynamic Programming. Athena Scientific, Belmont, MA. INRMM-MiD:206209 .
  3. Georgakakos, A.P., Marks, D.H., 1987. A new method for real-time operation of reservoir systems. Water Resour. Res., 23(7), pp. 1376–1390.
  4. Georgakakos, A.P., 1989. Extended Linear Quadratic Gaussian Control for the real-time operation of reservoir
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Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
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INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.