scholarly journals Generating Hydrants’ Configurations for Efficient Analysis and Management of Pressurized Irrigation Distribution Systems

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 204 ◽  
Author(s):  
Abdelouahid Fouial ◽  
Nicola Lamaddalena ◽  
Juan Antonio Rodríguez Díaz
Keyword(s):  
Distribution Systems ◽  
A Priori ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Irrigation Season ◽  
Peak Period ◽  
Decision Making Processes ◽  
Pressurized Irrigation ◽  
Effective Water

Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. One of the most challenging uncertainties in the operation of on-demand collective Pressurized Irrigation Distribution Systems (PIDSs) is to know, a priori, the number and the position of hydrants in simultaneous operation. To this end, a model was developed to generate close to reality operating hydrants configurations, with 15, 30 or 60 min time steps, by estimating the irrigation scheduling for the entire irrigation season, using climatic, crop and soil data. The model is incorporated in an integrated DSS called Decision Support for Irrigation Distribution Systems (DESIDS) and links two of its modules, namely, the irrigation demand and scheduling module and the hydraulic analysis module. The latter is used to perform two types of analyses for the performance assessment and decision-making processes. The model was used in a real case study in Italy to generate hydrants’ operation taking into consideration irrigation scheduling. The results show that during the peak period, hydrants simultaneity topped 62%. The latter created pressure deficit in some hydrants, thus reducing the volume of water supplied for irrigation by up to 87 m3 in a single hydrant during the peak demand day. The developed model proved to be an important tool for irrigation managers, as it provides vital information with great flexibility and the ability to assess and predict the operation of PIDSs at any period during the irrigation season.

scholarly journals Trends and Challenges in Irrigation Scheduling in the Semi-Arid Area of Spain

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 785 ◽  
Author(s):  
Irene Fernández García ◽  
Sergio Lecina ◽  
M. Carmen Ruiz-Sánchez ◽  
Juan Vera ◽  
Wenceslao Conejero ◽  
...  
Keyword(s):  
Irrigation System ◽  
Irrigation Scheduling ◽  
Living Standards ◽  
Spatiotemporal Variability ◽  
Irrigated Agriculture ◽  
Decision Making Processes ◽  
Current Water ◽  
Economic Capacity ◽  
Review Current ◽  
Semi Arid

A growing international human population and rising living standards are increasing the demand for agricultural products. Under higher pressure over natural resources, environmental concerns are increasing as well, challenging current water use decision-making processes in irrigated agriculture. Higher agricultural productivity means water should be applied more efficiently, which requires instant information on weather, soil, and plant conditions throughout the growing season. An information-based irrigation scheduling application tightened to the spatiotemporal variability of the fields is critical for enhancing the current irrigation system and making better irrigation scheduling decisions. The aim of this study is to review current irrigation scheduling methodologies based on two case studies (woody and field crops) located in semi-arid areas of Southeast Spain. We realize that optimal irrigation programming requires consistent investment in equipment, expenditure on operation and maintenance, and qualified technical and maintenance services. These technological approaches will be worthwhile in farms with low water availability, high profitability, and significant technical-economic capacity.

scholarly journals Middleware to Operate Smart Photovoltaic Irrigation Systems in Real Time

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1508 ◽  
Author(s):  
Rafael González Perea ◽  
Aida Mérida García ◽  
Irene Fernández García ◽  
Emilio Camacho Poyato ◽  
Pilar Montesinos ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Precision Agriculture ◽  
Renewable Energy Sources ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Real Field ◽  
Energy Sources ◽  
Irrigated Agriculture ◽  
Irrigation Season

Climate change, water scarcity and higher energy requirements and electric tariff compromises the continuity of the irrigated agriculture. Precision agriculture (PA) or renewable energy sources which are based on communication and information technologies and a large amount of data are key to ensuring this economic activity and guaranteeing food security at the global level. Several works which are based on the use of PA and renewable energy sources have been developed in order to optimize different variables of irrigated agriculture such as irrigation scheduling. However, the large amount of technologies and sensors that these models need to be implemented are still far from being easily accessible and usable by farmers. In this way, a middleware called Real time Smart Solar Irrigation Manager (RESSIM) has been developed in this work and implemented in MATLABTM with the aim to provide to farmers a user-friendly tool for the daily making decision process of irrigation scheduling using a smart photovoltaic irrigation management module. RESSIM middleware was successfully tested in a real field during a full irrigation season of olive trees using a real smart photovoltaic irrigation system.

scholarly journals A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 13 (20) ◽  
pp. 11355
Author(s):  
Saman Rabiei ◽  
Ehsan Jalilvand ◽  
Massoud Tajrishy
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Irrigation Scheduling ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Irrigated Agriculture ◽  
Irrigation Season ◽  
Surface Soil Moisture ◽  
Cropland Area ◽  
Sentinel 2

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.

scholarly journals Strategies for a Circular Economy in the Construction and Demolition Sector: Identifying the Factors Affecting the Recommendation of Recycled Concrete

2021 ◽  
Vol 13 (8) ◽  
pp. 4113
Author(s):  
Valeria Superti ◽  
Cynthia Houmani ◽  
Ralph Hansmann ◽  
Ivo Baur ◽  
Claudia R. Binder
Keyword(s):  
Circular Economy ◽  
A Priori ◽  
Recycled Concrete ◽  
Recycled Aggregates ◽  
Factors Affecting ◽  
Structural Applications ◽  
Adoption Of Innovation ◽  
Decision Making Processes ◽  
Behavioural Factors ◽  
High Level

With increasing urbanisation, new approaches such as the Circular Economy (CE) are needed to reduce resource consumption. In Switzerland, Construction & Demolition (C&D) waste accounts for the largest portion of waste (84%). Beyond limiting the depletion of primary resources, implementing recycling strategies for C&D waste (such as using recycled aggregates to produce recycled concrete (RC)), can also decrease the amount of landfilled C&D waste. The use of RC still faces adoption barriers. In this research, we examined the factors driving the adoption of recycled products for a CE in the C&D sector by focusing on RC for structural applications. We developed a behavioural framework to understand the determinants of architects’ decisions to recommend RC. We collected and analysed survey data from 727 respondents. The analyses focused on architects’ a priori beliefs about RC, behavioural factors affecting their recommendations of RC, and project-specific contextual factors that might play a role in the recommendation of RC. Our results show that the factors that mainly facilitate the recommendation of RC by architects are: a senior position, a high level of RC knowledge and of the Minergie label, beliefs about the reduced environmental impact of RC, as well as favourable prescriptive social norms expressed by clients and other architects. We emphasise the importance of a holistic theoretical framework in approaching decision-making processes related to the adoption of innovation, and the importance of the agency of each involved actor for a transition towards a circular construction sector.

scholarly journals Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings

2021 ◽  
Vol 11 (2) ◽  
pp. 500
Author(s):  
Fabrizio Pilo ◽  
Giuditta Pisano ◽  
Simona Ruggeri ◽  
Matteo Troncia
Keyword(s):  
Energy Demand ◽  
Data Analytics ◽  
Distribution Systems ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
A Priori ◽  
Energy Transition ◽  
Smart Meters ◽  
Large Databases ◽  
A New Technique

The energy transition for decarbonization requires consumers’ and producers’ active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO).

scholarly journals Combined simulation and optimization framework for irrigation scheduling in agriculture fields

2021 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernàndez-Garcia ◽  
Gema Rodrigo ◽  
Francesc Ferrer ◽  
Josep Maria Villar
Keyword(s):  
Crop Yields ◽  
Root Zone ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Traditional Methods ◽  
Simulation And Optimization ◽  
Optimization Framework ◽  
Net Margin ◽  
Combined Simulation

AbstractIn the context of growing evidence of climate change and the fact that agriculture uses about 70% of all the water available for irrigation in semi-arid areas, there is an increasing probability of water scarcity scenarios. Water irrigation optimization is, therefore, one of the main goals of researchers and stakeholders involved in irrigated agriculture. Irrigation scheduling is often conducted based on simple water requirement calculations without accounting for the strong link between water movement in the root zone, soil–water–crop productivity and irrigation expenses. In this work, we present a combined simulation and optimization framework aimed at estimating irrigation parameters that maximize the crop net margin. The simulation component couples the movement of water in a variably saturated porous media driven by irrigation with crop water uptake and crop yields. The optimization component assures maximum gain with minimum cost of crop production during a growing season. An application of the method demonstrates that an optimal solution exists and substantially differs from traditional methods. In contrast to traditional methods, results show that the optimal irrigation scheduling solution prevents water logging and provides a more constant value of water content during the entire growing season within the root zone. As a result, in this case, the crop net margin cost exhibits a substantial increase with respect to the traditional method. The optimal irrigation scheduling solution is also shown to strongly depend on the particular soil hydraulic properties of the given field site.

Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. F25-F34 ◽  
Author(s):  
Benoit Tournerie ◽  
Michel Chouteau ◽  
Denis Marcotte
Keyword(s):  
Apparent Resistivity ◽  
A Priori ◽  
Shift Factor ◽  
Static Shift ◽  
Geostatistical Model ◽  
Data Set ◽  
Resistivity Sounding ◽  
The Difference ◽  
Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

scholarly journals Modeling and Estimating Host Country Values in International Projects to Facilitate In-Country Value Creation

2021 ◽  
Vol 13 (10) ◽  
pp. 5592
Author(s):  
Ludovic-Alexandre Vidal ◽  
Franck Marle ◽  
Mathieu Dernis
Keyword(s):  
Host Country ◽  
Oil And Gas ◽  
Value Chains ◽  
Host Countries ◽  
Complex Projects ◽  
Domain Mapping ◽  
International Projects ◽  
Decision Making Processes ◽  
Industrial Needs

International companies are more and more seeking to act proactively by proposing In-Country Value (ICV) strategies to create sustainable local values in the host countries in which they carry out projects. Still, such sustainable local values are complex to identify because they are often indirectly related to their own value chains, project activities, and outcomes. There are, therefore, both theoretical and industrial needs to model and estimate sustainable values brought by complex projects in host countries, considering direct and indirect effects. In this paper, a systems thinking-based approach combined with a frequency analysis first permitted to build up a model of the sustainable values created by the project in a host country. Then, after underlining the complexity of such a model, a Domain Mapping Matrix (DMM) approach was proposed to help build a process to estimate project impacts in terms of ICV creation. An application to a case study built up with an industrial practitioner (an oil and gas company) permitted to test and validate the overall model and approach. It notably showed how such a model permitted to facilitate discussions among stakeholders and laid the foundations of ICV creation-oriented decision-making processes.

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