A Novel Hybrid Technique of Integrating Gradient-Boosted Machine and Clustering Algorithms for Lithology Classification     

Asante-Okyere  Solomon  Shen  Chuanbo  Ziggah  Yao Yevenyo  Rulegeya  Mercy Moses  Zhu  Xiangfeng 《Natural Resources Research》2020,29(4):2257-2273

Natural Resources Research - The significant body of research on lithology identification in recent years has laid emphasis on the improvement of classification performance using hybrid machine...  相似文献   

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates (\phi ,\lambda,h) to Cartesian Coordinates (X,Y, Z)     

Yao Yevenyo Ziggah  Hu Youjian  Xianyu Yu  Laari Prosper Basommi 《Mathematical Geosciences》2016,48(6):687-721

  相似文献   

Forecasting of Horizontal Gas Well Production Decline in Unconventional Reservoirs using Productivity,Soft Computing and Swarm Intelligence Models     

Brantson  Eric Thompson  Ju  Binshan  Ziggah  Yao Yevenyo  Akwensi  Perpetual Hope  Sun  Yan  Wu  Dan  Addo  Bright Junior 《Natural Resources Research》2019,28(3):717-756

Natural Resources Research - The most widely used production decline forecasting tools are numerical reservoir simulation, material balance estimates and advanced methods of production decline...  相似文献   

Performance evaluation of artificial neural networks for planimetric coordinate transformation—a case study,Ghana     

Yao Yevenyo Ziggah  Hu Youjian  Alfonso Tierra  Ahmed Amara Konaté  Zhenyang Hui 《Arabian Journal of Geosciences》2016,9(17):698

Two national horizontal geodetic datums, namely, the Accra and Leigon datum, have been the only available datum used in Ghana. These two datums are non-geocentric and were established based on astro-geodetic observations. Relating these different geodetic datums mostly involves the use of conformal transformation techniques which could produce results that are not very often satisfactory for certain geodetic, surveying and mapping purposes. This has been ascribed to the incapability of the conformal models to absorb more of the heterogeneous and local character of deformations existing within the local geodetic networks. Presently, application of new approaches such as artificial neural network (ANN) is highly recommended. Whereas the ANN has been gaining much popularity to solving coordinate transformation-related problems in recent times, the existing researches carried out in Ghana have shown that only three-dimensional conformal transformation methods have been utilized. To the best of our knowledge, plane coordinate transformation between the two local geodetic datums in Ghana has not been investigated. In this paper, an attempt has been made to explore the plane coordinate transformation performance of two different ANN approaches (backpropagation neural network (BPNN) and radial basis function neural network (RBFNN)) compared with two different traditional techniques (six- and four-parameter models) in the Ghana national geodetic reference network. The results revealed that transforming plane coordinates from Leigon to Accra datum, the RBFNN was better than the BPNN and traditional techniques. Transforming from Accra to Leigon datum, both the BPNN and RBFNN produced closely related results and were better than the traditional methods. Therefore, this study will create the opportunity for Ghana to recognize the significance and strength of the ANN technology in solving coordinate transformation problems.  相似文献