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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Perfecting a Video Game with Game Metrics
Muhammad Sani Gaya
Kano University of Science and Technology
Computer Science & IT Engineering

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Estimation of water quality index using artificial intelligence approaches and multi-linear regression Muhammad Sani Gaya; Sani Isah Abba; Aliyu Muhammad Abdu; Abubakar Ibrahim Tukur; Mubarak Auwal Saleh; Parvaneh Esmaili; Norhaliza Abdul Wahab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.66 KB) | DOI: 10.11591/ijai.v9.i1.pp126-134

Abstract

Water quality index is a measure of water quality at a certain location and over a period of time. High value indicates that the water is unsafe for drinking and inadequate in quality to meet the designated uses. Most of the classical models are unreliable producing unpromising forecasting results. This study presents Artificial Intelligence (AI) techniques and a Multi Linear Regression (MLR) as the classical linear model for estimating the Water Quality Index (WQI) of Palla station of Yamuna river, India. Full-scale data of the river were used in validating the models. Performance measures such as Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Determination Coefficient (DC) were utilized in evaluating the accuracy and performance of the models. The obtained result depicted the superiority of AI models over the MLR model. The results also indicated that, the best model of both ANN and ANFIS proved high improvement in performance accuracy over MLR up to 10% in the verification phase. The difference between ANN and ANFIS accuracy is negligible due to a slight increment in performance accuracy indicating that both ANN and ANFIS could serve as reliable models for the estimation of WQI.
Medium term load demand forecast of Kano zone using neural network algorithms Huzaimu Lawal Imam; Muhammad Sani Gaya; G. S. M. Galadanci
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14032

Abstract

Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand.
Co-Authors Abubakar Ibrahim Tukur Aliyu Muhammad Abdu G. S. M. Galadanci Huzaimu Lawal Imam Mubarak Auwal Saleh Norhaliza Abdul Wahab Parvaneh Esmaili Sani Isah Abba