The influence of aeration on the emitter clogging of Yellow River water drip irrigation was evaluated, and reasonable measures to slow down the emitter clogging were investigated. A short-cycle intermittent irrigation test and sediment settlement test were performed under the conditions of aeration and no aeration. The results showed that aeration accelerated the settlement of sediment at the inlet of the flow channel, increased the clogging rate of the flow channel inlet and exacerbated the clogging of the emitter. With the decrease of sediment particle size, aeration increased sediment deposition and emitter clogging. With the increase fencing inlet area, the effect of aeration on sediment settlement and emitter clogging decreased. Under aeration conditions, the upward position of the emitter outlet significantly reduced the mass of emitter blockage and increased the emitter flow compared with the downward positioning of the outlet. Thus, the recommendation is to select an emitter with a large area of fencing inlet, and the outlet of the emitter should be positioned upwards in order to improve the anticlogging performance of the emitter for Yellow River water aerated drip irrigation.
Variable precise fertigation is critical to precision irrigation. The question of how to monitor the combination of fertilizer concentration and variable irrigation components as accurately as possible is challenging. The primary goal of this study is to develop accurate prediction models integrated with machine learning (ML) to predict the concentration of each type of fertilizers in mixed variable-rate fertigation used for precision irrigation. First, the feasibility of predicting of fertilizer concentration by monitoring physical parameters such as electrical conductivity (EC), acidity (pH) and temperature in mixed variable-rate fertigation was confirmed. 11 selected ML algorithms were applied to develop regression models that can accurately predict each fertilizer concentration of the mixed fertilizer compared to the classical multivariate linear regression (MLR). In addition, cubic spline interpolation (CSI) was used to densify the data sets, and K-fold cross-validation was employed to fairly evaluate the generalization ability (GA) of these models. The statistical and diagnostic analyzes revealed the superiority of ML especially SVM, KNN, ETs, and MLP over MLR in predicting each type of fertilizer concentration in mixed variable-rate fertigation with an R2 range of 0.9499 ~ 0.9970 and an RMSE range of 0.0852 ~ 0.4434 g/L, better than MLR with an R2 range of 0.8544 ~ 0.9425 and an RMSE range of 0.3752 ~ 0.7559 g/L. Moreover, the contribution of CSI to the modeling accuracy was confirmed, but the sensitivity of the models to EC and pH increased with the data from CSI and the tuning of the model hyper-parameter. Overall, the feasibility and performance of the ML models for predicting mixed fertilizer concentration by monitoring temperature, EC, and pH indicate that the presented ML models have significant application potential for irrigation and fertilization monitoring management of mixed variable-rate fertigation in precision irrigation with high-precision sensor technology.
Risk assessment of drip irrigation system emitter clogging is critical for the system's safe operation. In this paper, the emitter clogging risk and the calculation method are proposed based on risk theory and fuzzy comprehensive evaluation method to quantify the emitter clogging risk during drip irrigation system operation. Moreover, dynamic Bayesian network and emitter clogging degree monitoring data are combined to evaluate the drip irrigation system's emitter clogging risk. The relationship between drip flow channel structure, drip irrigation water quality, drip irrigation system operation, management mode, drip irrigation environment, and the emitter clogging risk of drip irrigation system is established. Based on the established relationship, the influence probability of different influencing factors on emitter clogging is obtained by expert experience and the Fuzzy membership function. Lastly, an emitter-clogging risk level table of the drip irrigation system is constructed. The results show that the model can better reflect the emitter clogging risk of the drip irrigation system and replace the occurrence probability of emitter clogging with fuzzy probability. In addition, the proposed model can quantitatively evaluate the probability of emitter clogging while risk factors can be identified, prevented, and controlled quickly and accurately. The sensitivity analysis shows that employing a fuzzy comprehensive evaluation method to calculate the probability of emitter clogging is reasonable and feasible. A risk level table can be employed to clarify the clogging risk status of the drip irrigation system, which can provide decision support and early warning treatment for drip irrigation system operation and management according to different clogging risk levels. Finally, corresponding anti-clogging measures can improve the system's life and operation efficiency.
To quantitatively evaluate the anti-clogging performance of labyrinth channel emitters, literary data were used to study the clogging of different emitters under different conditions. The results showed that the emitter clogging comprehensive evaluation index (I a ) can be used as an inherent property of labyrinth flow channel emitters, denoted as the anti-clogging ability characterization index, which is affected by flow channel structure parameters and dimensions. The larger I a is, the more likely it is for the emitter to be clogged and for clogging to be more serious. The emitter anti-clogging ability is affected by the emitter discharge (Q), flow path length (L), width (W), depth (D), crosssectional area (A, which equals W Â D), minimum sectional size (min(D, W)), etc. The I a value with Q, W, D, A and min(D, W) increases first decreases and then increases, and with L increases first increases and then decreasing, and a critical threshold range exists. I a can be estimated by Q, W, D, A and min(D, W) and can be used as an effective indicator to quickly estimate emitter anticlogging ability and emitter configuration in drip irrigation systems. In practical applications, an emitter with x < 0.47 (flow index) should be selected, and an emitter with I a < 0.45 has a better hydraulic performance and anti-clogging ability. To obtain a better anti-clogging ability, Q, W, D, A and min(D, W) should be 1.33-2.13 L/h, 0.71-0.90 mm, 0.55-0.71 mm, 0.33-0.68 mm 2 and 0.60-0.75 mm, respectively. When L is 26.47-58.31 mm, the emitter anticlogging ability is relatively low. K E Y W O R D S anti-clogging ability, characteristic parameters, evaluate, influencing factors, labyrinth channel emitter Résumé Pour évaluer quantitativement la performance anti-colmatante des buses individuelles à canal labyrinthe, des données littéraires ont été utilisées pour étudier le colmatage de différentes buses individuelles dans différentes conditions. Les résultats ont montré que l'indice d'évaluation globale du colmatage Article title in French: Facteurs d'influence et methodes de caracterisation de la capacite anti-colmatante des buses individuelles a canal labyrinthe.
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