Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha−1 and were smaller than those of MLR (1,068 kg ha−1) and null model (1,035 kg ha−1). These models outperformed the controls in other metrics including Pearson’s correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.

show abstract

“… 4 , 7 – 11 ] and soil water management indices [e.g. 14 , 15 ] should be examined to enhance model performance in future works.…”

Section: Resultsmentioning

confidence: 99%

“… 12 , 13 ] and to monitor evapotranspiration for water resources engineering [e.g. 14 , 15 ]. Some of these studies have analyzed the importance of predictor variables to improve models’ interpretability and predictive performance [ 7 , 9 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The MLR method is based on linear regression used for developing the association between dependent and independent variables. The commonly used equation for MLR is as follows (Malik et al 2021a):…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

show abstract

“…It crucially affects the various sectors of the ecological components of the earth, especially water resources availability, vegetation, agricultural and industrial production, wildlife, river basin systems, and socio-economic structures (Abdo, 2020;Ahmed et al, 2019;Hateffard et al, 2021). However, global literature on drought assessment indicates that extreme drought episodes are predicted to increase as climate change progresses (Deo et al, 2018;Malik et al, 2021). Therefore, the process of predicting and modelling characteristics of drought, including duration, intensity, and frequency, is a vital and urgent mechanism in the context of adequate water resources management for agricultural, industrial and service development.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

Pande

Kushwaha

Orimoloye

et al. 2023

Water Resour Manage

View full text Add to dashboard Cite

Precise assessment, monitoring and forecasting of drought phenomena are crucial and play a vital role in agriculture and water resources management in the semi-arid region. In this study, Standardized Precipitation Index (SPI) was used to predict the drought in the upper Godavari River basin, India. Ten combinations were used to predict three SPI timescales (i.e., . The historical data of SPI from 2000 to 2019 was divided into training (75 % of the data) and testing (25 % of the data) models for SPI prediction. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to estimate the SPI. The results were compared and analyzed using statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The main results showed that the SMO-SVM poly kernel model precisely predict the SPI-3 (R 2 = 0.819) and SPI-12 (R 2 = 0.968) values at Paithan station; the SPI-3 (R 2 = 0.736) and SPI-6 (R 2 = 0.841) values at Silload station, respectively.The SMO-SVM PUK kernel showed superiority in the prediction of SPI-6 (R 2 = 0.846) at Paithan station and SPI-12 (R 2 = 0.975) at the Silload station. The competition between SVM-SMO poly kernel and SVM-SMO PUK kernel was observed in the prediction of long setting time (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is superior in the estimation of SPI-3 at both stations. The results of the study showed the efficacy of the SVM-SMO algorithm with various kernel functions in the estimation of multiscale SPI and can be helpful in decision making for water resource management and tackle droughts in the semi-arid region of central India.

show abstract

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Cited by 28 publications

References 50 publications

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

Contact Info

Product

Resources

About