Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia

Abstract

This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data.

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

“…In this study, four different machine learning algorithms, namely multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and boosted decision tree (BDT), have been proposed to predict the changes in water quality parameters. More details about these models can be seen in (Choi et al 2018 ; Lai et al 2019 ; Jumin et al 2020 ; Muslim et al 2020 ). After developing the above-mentioned techniques, the performance of these techniques was evaluated based on comparing the actual and the predicted data from each technique and based on the relative error percent, which can measure the discrepancy between the predicted and the observed data (Fiyadh et al 2019 ): …”

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

“…Yanguang Fu et al 2019;Hao et al 2019). In addition, they require big ocean and atmospheric data and demand high processing power of super-computers that is beyond the capacity of conventional PCs (Muslim et al 2020). Further, tidal harmonic analysis requires processing tidal observations of many decades.…”

“…Sea Level over Malaysia Coast have been rising between 1.4 to 4.1 mm/yr (Muslim et al 2020;Sarkar et al 2014). Abdul Hadi Kamaruddin et al (2016) determined the magnitude and rate of long term (30 years) sea level rise between 1984 and 2013 using tidal data, with outcomes of 0.095 m and 3.67 6 0.15 mm/yr respectively.…”

“…An investigation of the elevation profile of the coastal zones using google earth ( Figure 2) revealed that the area comprises of low-elevation areas like most coastal regions. More than 80% of the coastal zone profile has elevation below 10 m. The low-lying coastal regions are highly populated, urbanized, and developed with dense infrastructure distribution (Hamid et al 2018;Muslim et al 2020), which makes them vulnerable to the harmful impacts of sea level rise. Its area encompasses Kuala Lumpur, the capital of Malaysia, and the new administrative centre, Putrajaya.…”

“…Various studies have reported that these variables have significant impacts on sea level variation (Sithara et al 2020). For instance, Longshore winds is a key variable influencing sea level variability on the eastern margins of the ocean (Sturges and Douglas 2011), through its role in moving warm water from one part of the sea to another (Muslim et al 2020). All the data are gridded monthly mean datasets between January 1993 and October 2019.…”