Simulating reference crop evapotranspiration with different climate data inputs using Gaussian exponential model

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash-Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit-cost ratio.

Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China

The accurate prediction of daily reference crop evapotranspiration (ET_O) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ET_O forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ET_O at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ET_O, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ET_O; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R² of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ET_O estimation in arid and semiarid regions of China, and this model can also serve as a reference for ET_O forecasting in similar regions.

Particle swarm optimization algorithm with Gaussian exponential model to predict daily and monthly global solar radiation in Northeast China

Reliable global solar radiation (R_s) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, R_s measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate R_s, particle swarm optimization (PSO) algorithm integrating Gaussian exponential model (GEM) was proposed for estimating daily and monthly global R_s in Northeast China. The PSO-GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997 to 2016 from four stations in Northeast China. The results showed that in different stations, the PSO-GEM with full climatic data as inputs showed the highest accuracy to estimate daily R_s with RMSE, RRMSE, MAE, R², and E_ns values of 1.045-1.719 MJ m^-2 d^-1, 7.6-12.7%, 0.801-1.283 MJ m^-2 d^-1, 0.953-0.981, and 0.946-0.977, respectively. The PSO-GEM showed the highest accuracy to estimate monthly R_s with RMSE, RRMSE, MAE, R², and E_ns values of 0.197-0.575 MJ m^-2 d^-1, 1.5-7.0%, 0.137-0.499 MJ m^-2 d^-1, 0.999-1, and 0.992-0.999, respectively. Overall, the PSO-GEM had the highest accuracy under different inputs and is recommended for modeling daily and monthly R_s in Northeast China.