Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ET_o) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ET_o. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ET_o modeling during the model training period, while in the testing period under scenarios S1(T_max) and S2 (T_max, and T_min), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ET_o in semi-arid climatic conditions.

Supporting evidences for vegetation-enhanced stormwater infiltration in bioretention systems: a comprehensive review

Stormwater mitigation efficiency of bioretention systems relies for a large part on their capacity to infiltrate rapidly received runoff. Within this context, the primary aim of this literature review was to clarify the vegetation influences on bioretention media hydraulic conductivity, with the ultimate goal of improving guidance on plant choice for system durability. A thorough synthesis of studies dealing with the comparison of plant species, functional types, or traits on infiltration-related processes in biofilters was achieved. Overall, results converged to a positive impact of plants on water infiltration and percolation, either under greenhouse or field conditions. In most cases, vegetation selection had a determining role in maintaining initial media infiltration rates, with in terms of improvement: turfgrass < prairie grass < shrubs < trees. Wind-induced movements of rigid foliage or stems are believed to avoid complete surface clogging. Species with thick, rhizomatous or fleshy (with maximum root diameter near the centimeter range), and tap or deep root systems could be preferred to maximize infiltration rates in permeable bioretention media. In fine-textured soils, higher specific root length, root length density, or mass density could also enhance infiltration. Root mass densities (0.1-2.2 kg.m³) were positively linked with infiltration rates in unlined systems while roots around 1 mm diameter would favor macropore-related preferential flows and increased hydraulic conductivity. Finally, implementation of high-diversity plant communities would ensure the presence of a more functionally rich vegetation community with species possessing adequate physiological adaptations (including root system architecture) to local environmental conditions for perennial cover and proper bioretention hydrological functioning.

Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation

Objective. Physical therapy is a common clinical treatment for patients with lumbar disc herniation. The study is aimed at exploring the feasibility of mathematical expression and curative effect prediction of physical therapy in patients with lumbar disc herniation using a logistic model and gradient boosting machine (GBM). Methods. A total of 142 patients with lumbar disc herniation were treated with physical therapy. The pain was evaluated by the visual analogue scale (VAS) before each treatment. The logistic model was used to conduct a global regression analysis on patients with lumbar disc herniation. The final results of the whole course of treatment were predicted by the measured values of 2-9 times of treatment. The GBM model was used to predict and analyze the curative effect of physical therapy. Results. The mathematical expression ability of the logistic regression model for patients with lumbar disc herniation undergoing physical therapy was sufficient, and the global determination coefficient was 0.721. The results would be better for more than five measurements. The AUC of GBM mode logistic regression analysis was 0.936 and 0.883, and the prediction effect is statistically significant. Conclusion. Both the logistic and GBM model can fully express the changes in patients with lumbar disc herniation during physical therapy.