Sains Malaysiana 45(1)(2016): 19–28

Artificial Neural Network Technique for Modeling of Groundwater Level in the Langat Basin, Malaysia

(Teknik Rangkaian Neural Buatan untuk Pemodelan Paras Air Bawah Tanah di Lembangan Langat, Malaysia)

MAHMOUD KHAKI*, ISMAIL YUSOFF, NUR ISLAMI & NUR HAYATI HUSSIN

Department of Geology, University of Malaya, 50603 Kuala Lumpur, Malaysia

Diserahkan: 20 Ogos 2014/Diterima: 8 November 2014

ABSTRACT

Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses.

Keywords: Artificial neural network (ANN); groundwater level; simulation

ABSTRAK

Ramalan variasi paras air bawah tanah adalah sangat diperlukan dalam pengurusan sumber air bawah tanah. Ketepatan ramalan paras air dapat membantu penggunaan secara praktikal dan optimum sumber air tanah. Objektif utama penggunaan rangkaian neuron buatan (ANN) adalah untuk mengkaji kebolehan suap ke hadapan, Elman dan Cascade rangkaian neuron ke hadapan dengan algoritma yang berbeza dalam menentukan paras air tanah di Lembangan Langat dari 2007 hingga 2013. Untuk memastikan ketepatan ramalan paras air tanah bulanan, keberkesanan pekali kecuraman dalam fungsi sigmoid model ANN yang dibangunkan dinilai dalam kajian ini. Prestasi model dinilai berdasarkan purata ralat kuasa dua (MSE) dan pekali korelasi (R). Keputusan menunjukkan bahawa teknik ANN adalah sangat sesuai digunakan dalam meramal paras air bawah tanah. Semua model yang dibangunkan menunjukkan keputusan yang boleh diterima. Berdasarkan pemerhatian, model rangkaian neuron ke hadapan yang dioptimumkan dengan algoritma Levenberg-Marquardt menunjukkan keputusan yang paling bermanfaat dengan nilai minimum MSE (0.048) dan nilai maksimum R (0.839) diperoleh daripada simulasi paras air bawah tanah. Kajian ini secara muktamadnya menunjukkan keupayaan ANN dalam memberikan penganggaran ketepatan terbaik dan analisis sensitiviti bernilai.

Kata kunci: Paras air bawah tanah; rangkaian neuron buatan (ANN); simulasi

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*Pengarang untuk surat-menyurat; email: mahmoud.khaki@gmail.com