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Effective Earth Radius Factor Prediction andMapping for Ondo State, South Western Nigeria

机译:尼日利亚西南部翁多州的有效地球半径因子预测和制图

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Accurate prediction and determination of the effective earth radius factor (k-factor) is critical for optimal performance in the design and planning of terrestrial line of sight communication links. In this work an artificial neural network (ANN) model is developed and used to predict k-factor values for four towns in Ondo State using satellite derived data. The towns are Okitipupa (6.5ºN, 4.78ºE), Ondo (7.11ºN, 4.83ºE), Akure (7.25ºN, 5.2ºE) and Ikare (7.52ºN, 5.75ºE). A feed forward back propagation ANN was implemented, thereafter trained, validated and tested using satellite derived data for the period from 1984 to 2002. Mean absolute error (MAE) was used to evaluate the performance of the ANN. The MAE values obtained were 0.0024, 0.0014, 0.002 and 0.003 for Okitipupa, Akure, Ikare and Ondo towns respectively. Contour map showing the predicted k-factor values interpolated over the map of Ondo state was plotted using Geographical Information System (GIS) techniques. The study concluded that ANN presents an effective means of predicting the average k-factor values over a geographical location.
机译:准确预测和确定有效地球半径因子(k因子)对于设计和规划地面视线通信链路的最佳性能至关重要。在这项工作中,开发了一个人工神经网络(ANN)模型,并使用卫星衍生数据将其用于预测翁多州四个镇的k因子值。城镇是奥基蒂帕(Okitipupa(6.5ºN,4.78ºE),Ondo(7.11ºN,4.83ºE),Akure(7.25ºN,5.2ºE)和Ikare(7.52ºN,5.75ºE)。实施了前馈传播ANN,然后使用卫星衍生数据对1984年至2002年进行了训练,验证和测试。平均绝对误差(MAE)用于评估ANN的性能。 Okitipupa,Akure,Ikare和Ondo镇的MAE值分别为0.0024、0.0014、0.002和0.003。使用地理信息系统(GIS)技术绘制等高线图,该轮廓图显示插在Ondo状态图上的预测k因子值。该研究得出结论,人工神经网络提供了一种预测地理位置上平均k因子值的有效方法。

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