Minor Losses through Start Connectors in Microirrigation Laterals: Dimensional Analysis and Artificial Neural Networks Approaches

Sobenko, Luiz Ricardo; Bombardelli, Wagner Wilson Ávila; Camargo, Antônio Pires de; Frizzone, José Antônio; Duarte, Sérgio Nascimento

doi:10.1061/(asce)ir.1943-4774.0001466

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…This operation normalizes the range of available data between zero and one and speeds up the learning operation. It also prevents the dominance of larger features in calculations by forming a set with a determined distance according to Equation (11) (Sobenko et al. , 2020).…”

Section: Methodsmentioning

confidence: 99%

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Sohrabi

Noorzai

2022

ECAM

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PurposeThe present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.Design/methodology/approachThe cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.FindingsThe findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.Originality/valueThis research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

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Section: Methodsmentioning

confidence: 99%

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Sohrabi

Noorzai

2022

ECAM

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“…The uniformity coefficients of the irrigation system determined in the evaluations were all higher than 90% (Table 1), being classified in the literature (Bralts, 1986;Sobenko et al, 2020) as excellent. The average flow rate measured in the drippers was 1.03 L/h and coincided with the value declared by the manufacturer for the established pressure.…”

Section: Uniformity Coefficientsmentioning

confidence: 96%

Development of a Low-Cost Open-Source Platform Connected to the Internet for Acquisition of Environmental Parameters and Soil Moisture

Pinto¹,

Camargo²,

Duarte³

2021

Eng. Agríc.

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The development of the Internet and the technologies associated with it has allowed disseminating and cheapening of communication equipment, prototyping services, electronic sensors, and all types of devices. Agriculture has benefited from these technological advances to boost its productivity and profitability. This study presents the development of a data acquisition and device control platform to obtain, in real-time and remotely, information from the field for decision-making and process automation. All electronic components are low-cost and "open hardware", and the software is "opensource". The developed platform was validated during a development cycle of two lettuce varieties (Japanese and crisp), in which the soil water matric potential was monitored at two depths (10 and 25 cm), while solar irradiation, air temperature, and soil temperature were evaluated only to monitor the cycle. The platform automatically and satisfactorily controlled the applied irrigation depths using only the data of matric potential by activating a solenoid valve and made the information from the sensors available on the ThingSpeak Internet of Things (IoT) platform.

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Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches

et al. 2023

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The presence of emitters along the lateral, as well as of connectors along the manifold, causes additional local head losses other than friction losses. An accurate estimation of local losses is of crucial importance for a correct design of microirrigation systems. This paper presents a procedure to assess local head losses caused by 6 lateral start connectors of 32- and 40-mm nominal diameter each under actual hydraulic working conditions based on artificial neural networks (ANN) and gene expression programming (GEP) modelling approaches. Different input–output combinations and data partitions were assessed to analyse the hydraulic performance of the system and the optimum training strategy of the models, respectively. The range of the head losses in the manifold (hsM) is considerable lower than in the lateral (hsL). hsM increases with the protrusion ratio (s/S). hsL does not decrease for a decreasing s/S. There is a correlation between hsL and the Reynolds number in the lateral (ReL). However, this correlation might also be dependent on the flow conditions in the manifold before the derivation. The value of the head loss component due to the protrusion might be influenced by the flow derivation. DN32 connectors and hsM present more accurate estimates. Crucial input parameters are flow velocity and protrusion ratio. The inclusion of friction head loss as input also improves the estimating accuracy of the models. The range of the indicators is considerably worse for DN40 than for DN32. The models trained with all patterns lead to more accurate estimations in connectors 7 to 12 than the models trained exclusively with DN40 patterns. On the other hand, including DN40 patterns in the training process did not involve any improvement for estimating the head losses of DN32 connectors. ANN were more accurate than GEP in DN32. In DN40 ANN were less accurate than GEP for hsM, but they were more accurate than GEP for hsL, while both presented a similar performance for hscombined. Different equations were obtained using GEP to easily estimate the two components of the local loss. The equation that should be used in practice depends on the availability of inputs.

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Minor Losses through Start Connectors in Microirrigation Laterals: Dimensional Analysis and Artificial Neural Networks Approaches

Cited by 6 publications

References 43 publications

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Development of a Low-Cost Open-Source Platform Connected to the Internet for Acquisition of Environmental Parameters and Soil Moisture

Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches

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