Using Machine Learning for Land Suitability Classification

Artificial intelligence and machine learning methods can be used to automate the land suitability classification. Multiple Classifier System (MCS) or ensemble methods are rapidly growing and receiving a lot of attention and proved to be more accurate and robust than an excellent single classifier in many fields. In this study a dataset based land suitability classification is addressed. It is done using a newly proposed ensemble classifier generation technique referred to as RotBoost, which is constructed by combining Rotation Forest and AdaBoost, and it is known to be the first time that RotBoost has been applied for suitability classification. The experiments conducted with the study area, Shavur plain, lies in the northern of Khuzestan province, southwest of Iran. It should be noted that suitability classes for the input data were calculated according to FAO method. This provides positive evidence for the utility of machine learning methods in land suitability classification especially MCS methods. The results demonstrate that RotBoost can generate ensemble classifiers with significantly higher prediction accuracy than either Rotation Forest or AdaBoost, which is about 99% and 88.5%, using two different performance evaluation measures

Sodium Valproate and Phenobarbitol: Weight Complications of Treatment in Epileptic Children

Objective The aim of this study was to evaluate and compare the effects of Na Valproate and Phenobarbital on changes in the weight of epileptic patients following treatment for their condition using the drugs mentioned.Materials and methodsSixty epileptics were assigned into two groups of 30 patients each, the case and controls. The diagnosis was made on the basis of the International League Against Epilepsy (ILAE) characteristics. BMI was defined. In the case group, the patients received 20mg/kg/day of Na Valproate, while the 30 controls received 5mg/kg/day of Phenobarbital for 6 months. Using the Mc Nemar and Chi-2 tests, BMI changes were compared after 6 months between the groups. Fisher's exact test was used to evaluate the role of age, sex, and primary weight on the weight increase due to Na Valproate usage.ResultsThere were no specific changes in age, sex, primary BMI and fatness between the 2 groups; in the case group, 20 patients(66.7%) and in the controls 4(13.3%) gained weight (PConclusionThe results indicate that epileptic children, aged over 10 years, and those who are overweight have more chances of gaining weight or becoming fatter, following treatment with Na Valproate. Further studies investigating the issue are warranted

Wastewater disposal and earthquake swarm activity at the southern end of the Central Valley, California

Fracture and fault zones can channel fluid flow and transmit injection-induced pore pressure changes over large distances (>km), at which seismicity is rarely suspected to be human induced. We use seismicity analysis and hydrogeological models to examine the role of seismically active faults in inducing earthquakes. We analyze a potentially injection-induced earthquake swarm with three events above M4 near the White Wolf fault (WWF). The swarm deviates from classic main aftershock behavior, exhibiting uncharacteristically low Gutenberg-Richter b of 0.6, and systematic migration patterns. Some smaller events occurred southeast of the WWF in an area of several disposal wells, one of which became active just 5 months before the main swarm activity. Hydrogeological modeling revealed that wastewater disposal likely contributed to seismicity via localized pressure increase along a seismically active fault. Our results suggest that induced seismicity may remain undetected in California without detailed analysis of local geologic setting, seismicity, and fluid diffusion

Neural network accuracy measures and data transforms applied to the seismic parameter estimation problem

The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field reservoir`s properties from remotely sensed seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN`s accuracy statistic from a finite sample set. In addition, we also show that an ANN`s classification accuracy is dramatically improved by transforming the ANN`s input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN`s convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data

Oil reservoir properties estimation using neural networks

This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters