Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological...

Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial intelligence to build more intelligent machines. Connectionism presents a cognitive theory based on simultaneously occurring, distributed signal activity via connections that can be represented numerically, where learning occurs by modifying connection strengths based on experience.Some advantages of the connectionist approach include its applicability to a broad array of functions, structural approximation to biological neurons, low requirements for innate structure, and capacity for graceful degradation. Some disadvantages include the difficulty in deciphering how ANNs process information, or account for the compositionality of mental representations, and a resultant difficulty explaining phenomena at a higher level.The success of deep learning networks in the past decade has greatly increased the popularity of this approach, but the complexity and scale of such networks has brought with them increased interpretability...

Accuracy of wave climate assessment is related to the length of available observed records of sea state variables of interest (significant wave height, mean direction, mean period, etc.). Data availability may be increased by record extension methods. In the paper, we investigate the use of artificial neural networks (ANNs) fed with reanalysis wind data to extend an observed time series of significant wave heights. In particular, six-hourly 10 m a.s.l. u - and v - wind speed data of the NCEP/NCAR Reanalysis I (NRA1) project are used to perform an extension of observed significant wave height series back to 1948 at the same time resolution. Wind for input is considered at several NRA1 grid-points and at several time lags as well, and the influence of the distance of input points and of the number of lags is analyzed to derive best-performing models, conceptually taking into account wind fetch and duration. Applications are conducted for buoys of the Italian Sea Monitoring Network of different climatic features, for which more than 15 years of observations are available. Results of the ANNs are compared to those of state-of-the-art wave reanalyses NOAA WAVEWATCH III/CFSR and ERA-Interim, and indicate that model performs slightly better than the former, which in turn outperforms the latter. The computational times for model training on a common workstation are typically of few hours, so the proposed method is potentially appealing to engineering practice.

Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors, usually managed as thematic data within geographic information systems (GIS). In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. Other more refined methods, based on the principle that the present and the past are keys to the future, have also been developed, thus allowing less subjective analyses in which landslide susceptibility is assessed by statistical relationships between past landslide events and hillslope instability factors. The objective of this research is to define a method with the ability to forecast landslide susceptibility through the application of Artificial Neural Networks (ANNs). The Riomaggiore catchment, a subwatershed of the Reno River basin located in the Northern Apennines (Italy), was chosen as an ideal test site, as it is representative of many of the geomorphological settings within this region. In the present application, two different ANNs, used in classification problems, were set up and applied: one belonging to the category of Multi-Layered Perceptron (MLP) and the other to the Probabilistic Neural Network (PNN) family. The hillslope factors that have been taken into account in the analysis were the following: (a) lithology, (b) slope angle, (c), profile curvature, (d) land cover and (e) upslope contributing area. These factors have been classified on nominal scales, and their intersection allowed 3342 homogeneous domains (Unique Condition Unit, UCU) to be singled out, which correspond to the terrain units utilized in this analysis. The model vector used to train the ANNs is a subset of that derived from the production of Unique Condition Units and consists of 3342 records organized in input and output variable vectors. In particular, the hillslope factors, once classified on nominal scales as binary numbers, represent the 19 input variables, while the presence/absence of a landslide in a given terrain unit is assumed to be the output variable. The comparison between the most up-to-date landslide inventory of the Riomaggiore catchment and the hazardous areas, as predicted by the ANNs, showed satisfactory results (with a slight preference for the MLP). For this reason, this is an encouraging preliminary approach towards a systematic introduction of ANN-based statistical methods in landslide hazard assessment and mapping.