A hybrid computational intelligence approach to groundwater spring potential mapping

© 2019 by the authors. This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely "AB-ADTree", for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including singleADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB-ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Novel hybrid evolutionary algorithms for spatial prediction of floods

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery

Landslide susceptibility modeling based on GIS and novel bagging-based Kernel logistic regression

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the overfitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas

A Model for Small Group Ministry in the North Zambia Field of the Seventh-day Adventist Church

Purpose The purpose of this dissertation is to develop a contextualized model for small group ministry in the North Zambia Field to enhance the retention of new members. Statement of Problem While the numbers of members being baptized annually are encouraging indeed, the percentage of members dropping out of the church annually is a cause of great concern. Between 1996 and 2006, the North Zambia Field, through baptism or profession of faith, had 72,652 converts, an annual average of 7,265 converts. However, 17,303 or 23.82 percent of those added and 2.70 percent of the total membership dropped out of the church through apostasy and going missing.1 This annual loss of church membership poses a serious problem for church growth. The largest number of members dropping out of the church in the North Zambia Field consists of newly baptized members brought into the church through various evangelistic activities. Many of these members coming into the church are unchurched or from different denominations in Zambia. When these new believers come into the Seventh-day Adventist church from such varying backgrounds, there is a great need for spiritual nurturing, training for discipleship, and pastoral care as they bond with established church members and develop a sense of belonging in their local church families. However, because so many people are baptized at short evangelistic efforts, without first being grounded in the practices and doctrines of the Seventh-day Adventist Church, many new members either return to a life without faith or return to their previous churches. Some of the reasons they give for dropping out of the Seventh-day Adventist Church include: lack of spiritual nurture, lack of discipleship training and lack of pastoral care to help them build relationships and bond with established church members. One way to address this problem is to encourage churches to form small groups to spiritually nurture, train for discipleship, and provide pastoral care to help new members to assimilate. Methodology The resources used in doing this research come from the James White Library at Andrews University in Berrien Springs, Michigan. Other sources also include the Bible, the writings of Ellen G. White, certain officials of the North Zambia Field Office, and the General Conference of the Seventh-day Adventist Church website statistics for the Zambia Union Conference. In view of the fact that the researcher lived in the United States of America from 2003-2008, some of the research data was obtained by telephone interviews with people living in Zambia at the time. The interviews attempted to find out why the new members drop out of the church in the North Zambia Field-especially members who come into the church through evangelistic efforts. Research data was also collected from North Zambia Field departmental directors’ records such as the Secretary’s Quarterly Report. A questionnaire was mailed to district pastors in the North Zambia Field to discover the reasons why new members drop out of church. These data were integrated with the researcher’s first-hand experiences as president of the North Zambia Field from 2000-2002, and several years of experience as the Zambia Union ministerial secretary responsible for church growth. Expectations from the Project This researcher expects to start implementing the small group model for membership retention upon his return to Zambia in 2008. By God’s grace, he expects to see positive results as the model is implemented

Improving energy performance of the UK housing through the implementation of Passive House standards

The UK government has committed itself to achieve net-zero on Greenhouse Gas (GHG) emissions by 2050. The UK housing sector is one of the major contributors to GHGs and over 60% of the energy used in the UK residential sector relates to heating. Thanks to their extremely high fabric standards, Passive House strategies and standards can significantly reduce the heating energy demand by 80%. Yet, these strategies are not widely implemented in the UK compared to other European countries such as Germany. This paper aims to explain such strategies and assess the effects of upgrading a typical UK house to both Part L of the UK Building Regulations and Passive House standards to compare the energy performances before and after upgrading. The case study building is modelled in EnergyPlus and the energy performances are compared. The results reveal that the heating energy consumptions reduced significantly by over 78% when the Passive House standards were implemented

The Limitation Amount of Available Potassium for Wheat in a Loess Soil

The objective of this study was determining the most limiting plant growth factor in the wheat root zone dominated by illite in clay fraction and a high specific surface with ample ammonium acetate extractible potassium. A completely randomized block design with 4 replicates was used in Seyed Miran Research Farm (Gorgan) during 2009-2010 growing season. Treatments were mineral fertilizers (to achieve different levels of yields), gypsum (1000 Kg/ha calcium), calcium chloride (1000 Kg ha-1 Ca), urea (93 Kg ha-1 N) and potassium chloride (105 Kg ha-1 K) combined, gypsum (1000 Kg ha-1 Ca) and potassium chloride (105 Kg ha-1 K) combined, calcium chloride (1000 Kg ha-1 Ca) and potassium chloride (105 Kg ha-1 K) combined and control. Wheat cultivar (N-80-19) was planted in experimental site at 2009/12/9. The results showed that potassium is the most limiting plant growth factor in the site of the experiment. Electric diffuse double layer is expected to be truncated with a high specific surface soil in this area minimizing the soil solution-diffuse double layer interface for rapid potassium diffusion. The highest yield grain and straw with urea and potassium chloride showed a greater effect on plant and soil potassium concentrations. A greater potassium diffusion rate may be achieved as a result of greater concentration gradients between the exchange sites and soil solution by potassium fertilization and more potassium excess. Ammonium from urea diminished potassium fixation with illite and increased potassium root uptake

Studying the effect of KCl Addition on the Optical Properties and Morphological of the Solid Polymer Electrolyte film

Abstract: In this work polymer electrolyte based polyethylene oxide doped with potassium chloride films were prepared using the solution cast method. The structural property of doped PEO polymer electrolyte films was examined by XRD. The optical properties of samples are investigated by measuring optical absorption spectra in the wavelength range 190~800nm using UV-Vis spectroscopy and its optical energy band gaps are decreases with increasing the KCl content. The variation in film morphology was examined by SEM