Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process

Human Control Performance in Telemanipulation Experiences

Human Control Performance in Telemanipulation Experience

Biofeedback and Feedforward in Telerobotics Control

Biofeedback and Feedforward in Telerobotics Contro

Movimento e controllo nella telerobotica: feedback e feedforward

Movimento e controllo nella telerobotica: feedback e feedforwar

A neuro-fuzzy model to predict the inflow to the guardialfiera multipurpose dam (Southern Italy) at medium-long time scales

Intelligent computing tools based on fuzzy logic and artificial neural networks have been successfully applied in various problems with superior performances. A new approach of combining these two powerful tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Few studies have been undertaken to evaluate their performances in hydrologic modeling. Specifically are available rainfall-runoff modeling typically at very short time scales (hourly, daily or event for the real-time forecasting of floods) with in input precipitation and past runoff (i.e. inflow rate) and in few cases models for the prediction of the monthly inflows to a dam using the past inflows as input. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in the forecasting of the inflow to the Guardialfiera multipurpose dam (CB, Italy) at the weekly and monthly time scale. The latter has been performed both directly at monthly scale (monthly input data) and iterating the weekly model. Twenty-nine years of rainfall, temperature, water level in the reservoir and releases to the different uses were available. In all simulations meteorological input data were used and in some cases also the past inflows. The performance of the defined ANFIS models were established by different efficiency and correlation indices. The results at the weekly time scale can be considered good, with a Nash- Sutcliffe efficiency index E = 0.724 in the testing phase. At the monthly time scale, satisfactory results were obtained with the iteration of the weekly model for the prediction of the incoming volume up to 3 weeks ahead (E = 0.574), while the direct simulation of monthly inflows gave barely satisfactory results (E = 0.502). The greatest difficulties encountered in the analysis were related to the reliability of the available data. The results of this study demonstrate the promising potential of ANFIS in the forecasting of the short term inflows to a reservoir and in the simulation of different scenarios for the water resources management in the longer term

IMPIEGO DEL CONTENUTO IDRICO DEL SUOLO E DEL DEFLUSSO SUPERFICIALE PER LA STIMA DELLA PERDITA DI SUOLO PARCELLARE A SCALA DI EVENTO

Nel presente lavoro viene valutata la potenzialità di accoppiare la USLE con il contenuto d’acqua del suolo pre-evento o il deflusso stimato, per migliorare l’accuratezza della stima della perdita di suolo a scala di singolo evento erosivo. A tale scopo sono stati utilizzati due approcci per i quali la perdita di suolo e il fattore di erosività sono legati da una legge di potenza. Il primo è il modello USLE-MM con deflusso stimato da un modello afflussi deflussi, SCRRM, che importa dati di contenuto d’acqua. Il secondo approccio è quello del modello SM4E che utilizza i dati di contenuto d’acqua pre-evento per correggere il fattore di erosività della pioggia. I due modelli sono stati testati usando le misure effettuate sulle parcelle di 22 m realizzate alle stazioni sperimentali di Masse e Sparacia. I risultati mostrano che la USLE-MM, che impiega misure di deflusso, conduce alle migliori stime della perdita di suolo. In atto, la simulazione del deflusso o l’uso del contenuto idrico del suolo conducono a una performance del modello peggiore di quella riscontrabile con la USLE originaria. Ulteriori indagini dovranno essere effettuate per migliorare la previsione del deflusso a scala di parcella, facendo anche ricorso a un database di misure più esteso

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

This paper presents a comparison of different methodologies for monitoring the plants growth in a greenhouse. A 2D measurement based on Computer Vision algorithms and 3D shape measurements techniques (Structured light, LIDAR and photogrammetry) are compared. From the joined 2D and 3D data, an analysis was performed considering health plant indicators. The methodologies are compared among each other. The acquired data are then fed into Deep Learning algorithms in order to detect anomalies in plant growth. The final aim is to give an assessment on the image acquisition methodologies, selecting the most suitable to be used to create the Deep Learning model inputs saving time and resources