Optimized Design of Neural Networks for a River Water Level Prediction System.

In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10-3, which compares favorably with results obtained by alternative design.

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT.

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

Characterization and Differentiation between Olive Varieties through Electrical Impedance Spectroscopy, Neural Networks and IoT.

Electrical impedance has shown itself to be useful in measuring the properties and characteristics of agri-food products: fruit quality, moisture content, the germination capacity in seeds or the frost-resistance of fruit. In the case of olives, it has been used to determine fat content and optimal harvest time. In this paper, a system based on the System on Chip (SoC) AD5933 running a 1024-point discrete Fourier transform (DFT) to return the impedance value as a magnitude and phase and which, working together with two ADG706 analog multiplexers and an external programmable clock based on a synthesized DDS in a FPGA XC3S250E-4VQG100C, allows for the impedance measurement in agri-food products with a frequency sweep from 1 Hz to 100 kHz. This paper demonstrates how electrical impedance is affected by the temperature both in freshly picked olives and in those processed in brine and provides a way to characterize cultivars by making use of only the electrical impedance, neural networks (NN) and the Internet of Things (IoT), allowing information to be collected from the olive samples analyzed both on farms and in factories.