The problem of slow drilling in deep shale formations occurs worldwide causing significant expenses to the oil industry. Bit balling which is widely considered as the main cause of poor bit performance in shales, especially deep shales, is being drilled with water-based mud. Therefore, efforts have been made to develop a model to diagnose drilling effectivity. Hence, we arrived at graphical cor...

Wind speed (WS) is an important factor in wind power generation. Because of this, drastic changes the WS make it challenging to analyze accurately. Therefore, this study proposed a novel framework based on stacking ensemble machine learning (SEML) method. The application for modeling was developed at sixteen stations Iran. SEML method consists two levels. In particular, eleven (ML) algorithms s...

Climate change has an unprecedented impact on the world, and it is also important era proposition to be solved in China's development. Carbon emissions trading occupies position, which aims adapt global carbon neutrality context implement sustainable development requirements. The market mechanism of China deal with climate change, position. Based emission price data Guangdong Province from 2015...

As a fundamental technique that concerns several vision tasks such as image parsing, action recognition and clothing retrieval, human pose estimation (HPE) has been extensively investigated in recent years. To achieve accurate and reliable estimation of the human pose, it is well-recognized that the clothing attributes are useful and should be utilized properly. Most previous approaches, howeve...

The main aim of this work is the determination aromaticity in biochar from easier accessible parameters (e.g., elemental composition). To end, two machine learning models, including adaptive neurofuzzy inference system (ANFIS) and least-squares support vector (LSSVM), were used to predict constant form 98 dataset gathered earlier reported sources. outputs statistical showed that LSSVM model has...

Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage property, adversity agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal environmental damage. The aim of this study was assessing performance different machine learning methods in predicting Karkheh basin. To this, we used Support Vector Machine (...

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector (LSSVM), group method data handling neural network (GMDHNN) classification regression trees (CART) air forecasting using monthly Ast...

A reliability analysis method based on least squares support vector machines with an improved particle swarm optimization algorithm (IPSO-LSSVM) is proposed to calculate the of concrete gravity dams when explicit nonlinear limit-state functions are difficult obtain accurately. First, main failure modes and their influencing factors determined. Second, Latin hypercube sampling used create sample...