Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation.

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

Evaluation of AquaCrop and intelligent models in predicting yield and biomass values of wheat.

AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588 t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05 t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values ​​for the farms were 0.033 and 0.069 t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066 t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs.

Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques.

Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, rigid boundary channels are designed based on self-cleansing criteria. Recently, instead of using a single velocity value for design of the self-cleansing channels, more hydraulic parameters such as sediment, fluid, flow and channel characteristics are being utilized. In this study, two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Frp) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models' performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides, Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4, as the precise NF model, performs better than the best GEP model (GEP1) and regression equations. As a conclusion, the obtained results proved the suitable accuracy and applicability of the NF method in Frp estimation.

Coupling artificial neural network and sperm swarm optimization for soil temperature prediction at multiple depths.

Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lower depths, becomes imperative. While recent models have demonstrated adeptness in predicting ST, in general, their error decreases with increasing depth, so that they had the lowest error at a depth of 100 cm. Addressing this gap, our study pioneers a novel hybrid model that excels in accurate daily ST prediction as it delves deeper. The models deployed encompass the multilayer perceptron (MLP) and an enhanced version, MLP coupled with the Sperm Swarm Optimization Algorithm (MLP-SSO). These models prognosticate daily ST across varying depths (5-100 cm), leveraging meteorological parameters such as air temperature, relative humidity, wind speed, sunshine hours, and precipitation. These parameters are anchored to the Ahvaz and Sabzevar synoptic stations in Iran, spanned over the period from 1997 to 2022. Evaluation of our research outcomes unveils that the root mean square error (RMSE) witnesses its most substantial reduction at a depth of 100 cm. For instance, at the Ahvaz station, the MLP-SSO model diminishes the RMSE value from 1.25 to 1.12 °C, in contrast to the MLP model. Similarly, at the Sabzevar station, the RMSE value drops from 1.78 to 1.49 °C using the coupled MLP-SSO model. These results robustly highlight the considerable enhancement brought about by the utilization of the MLP-SSO model, clearly surpassing the performance of the standalone MLP model. This emphasizes the potential and promise of the MLP-SSO model for future investigations, offering insights that can significantly advance the domain of soil temperature prediction and its implications for agricultural decision-making.

Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning.

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines.