Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning

The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics.The ability to rapidly and accurately predict the piezoelectric coefficient(d_(33))is of much practical importance for exploring high-performance piezoelectric ceramics.In this work,a data-driven approach combining feature engineering,statistical learning,machine learning(ML),experimental design,and synthesis is trialed to investigate its accuracy in predicting d_(33) of potassium-sodium-niobate(K,Na)NbO_(3),KNN)-based ceramics.The atomic radius(AR),valence electron distance(DV)(Schubert),Martynov-Batsanov electronegativity(EN-MB),and absolute electronegativity(EN)are summarized as the four most representative features in describing d_(33) out of all 27 possible features for the piezoelectric ceramics.These four features contribute greatly to regression learning for predicting d_(33) and classification learning for distinguishing polymorphic phase boundary(PPB).The ML method developed in this work exhibits a high accuracy in predicting d_(33) of the piezoelectric ceramics.An example of KNN combined with 6 mo1%LiNbO_(3)demonstrates d_(33)3 of 184 pC/N,which is highly consistent with the predicted result.This work proposes a novel feature-oriented guideline for accelerating the design of piezoelectric ceramic systems with large d_(33),which is expected to be widely used in other functional materials.

Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems

This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)system.Due to the dynamic nature of weather conditions,the energy generation of PV systems is non-linear.Since there is no specific method for effectively dealing with the non-linear data,the use of ML methods to operate the PV system at its maximum power point(MPP)is desirable.A strategy based on the decision-tree(DT)regression ML algorithm is proposed in this work to determine the MPP of a PV system.The data were gleaned from the technical specifications of the PV module and were used to train and test the DT.These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature.The boost converter duty cycle was determined using predicted values.The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m^(2) irradiance and a temperature of 25℃.The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such asβ-MPPT,cuckoo search and artificial neural network results.From the proposed algorithm,efficiency has been improved by>93.93%in the steady state despite erratic irradiance and temperatures.