GeoCorpora: building a corpus to test and train microblog geoparsers

In this article, we present the GeoCorpora corpus building framework and software tools as well as a geo-annotated Twitter corpus built with these tools to foster research and development in the areas of microblog/Twitter geoparsing and geographic information retrieval. The developed framework employs crowdsourcing and geovisual analytics to support the construction of large corpora of text in which the mentioned location entities are identified and geolocated to toponyms in existing geographical gazetteers. We describe how the approach has been applied to build a corpus of geo-annotated tweets that will be made freely available to the research community alongside this article to support the evaluation, comparison and training of geoparsers. Additionally, we report lessons learned related to corpus construction for geoparsing as well as insights about the notions of place and natural spatial language that we derive from application of the framework to building this corpus.     

An intelligent spatial land use planning support system using socially rational agents

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained.

This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents.     

Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets,a case study in seridune deposit,Iran

This paper describes the application of the knowledge-based fuzzy logic method to integrate various exploratory geo-dataset in order to prepare a mineral prospectivity map (MPM) for copper exploration. Different geophysical layers which are derived from the magnetic and the electrical surveys, along with the ones extracted from the background geology (i.e., lithology, fault and alteration) and geochemical data are incorporated in such process. Seridune copper deposit located in the Kerman province of Iran is the case study to delineate its high potential zones of Cu-bearing mineralization for drilling additional boreholes. Four layers from the magnetic data involving upward continuation, analytic signal, reduced to pole and pseudo gravity are assigned in the multi-disciplinary geo-dataset to locate the intrusive complexes responsible for Cu mineralization. The apparent resistivity, chargeability and sulfide factor layers acquired from geo-electrical data are also included in the final preparation of MPM. Then the normalized weights of seven geophysical, three geological and one geochemical evidential layers as main criteria are determined based upon the knowledge of expert decision makers. Fuzzy operators (i.e., Sum and Gamma) are applied to integrate these exploratory features. To evaluate the performance and applicability of the approach, the productivity of the drilled boreholes (Cu concentration multiplied by ore thickness) are used to validate the produced MPMs. It is shown that an optimum correlation coefficient of 0.86 exists between the MPM values and Cu productivity criterion along drilled boreholes.     

Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates

Because of scarcity and high variability of rainfall in arid areas, from one hand, reliable prediction of precipitation in such regions is considerably difficult. Furthermore, in some cases, shortage of observation data and several other limitations may intensify complexity of the forecasting. On the other hand, these regions highly suffer from low availability of water which necessitates development of an appropriate modeling approach to provide as precise as possible predictions of precipitation. Artificial neural networks (ANNs) are expected to be a powerful tool in capturing and analyzing high interannual variability of precipitation in arid climates and, subsequently, in proper prediction of precipitation fluctuations in the future. The end of this paper is to improve ANN predictions of precipitation in arid climates using better training of the network. To this end, two approaches were applied. In the first one, just the rainfall monthly data were considered as input. In the second approach, in addition to precipitation, several exogenous variables of precipitation are considered as input to predict precipitation. The chosen exogenous parameters are either effective on or relevant to the precipitation patterns. Then, several lag times, hidden layer sizes, and training algorithms for different running sums are used in order to produce best forecasts. It was shown that the performance of networks increases significantly by importing more external factors as inputs. The bigger time scales also exhibited better performances. In all the five time scales, smaller lag times (especially one month), bigger hidden layer sizes (especially between 31 and 40), and GDX training algorithm presented the best performance. The highest obtained performance was presented by the network with 10 inputs, 1 month lag, 36 hidden layers, and CGF training method in 18 months running sum with R ² of 0.93.     

Spatio-Geologically Informed Fuzzy Classification: An Innovative Method for Recognition of Mineralization-Related Patterns by Integration of Elemental, 3D Spatial,and Geological Information

Natural Resources Research - Recognition and mapping of mineralization-related patterns in geochemical data is a key computational analysis to achieve a predictive model of prospectivity for...