Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS

The main goal of this study is to investigate the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Langat basin in Malaysia using geographical information system. So far, the approach of probabilistic frequency ratio model has not yet been used to delineate groundwater potential in Malaysia. Moreover, this study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as elevation, slope, curvature, river, lineament, geology, soil, and land use for this region. Eight groundwater-related factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. About 68 groundwater data with high potential yield values of ≥11 m³/h were randomly selected using statistical software of SPSS. Then, the groundwater data were randomly split into a training dataset 70 % (48 borehole data) for training the model and the remaining 30 % (20 borehole data) was used for validation purpose. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The validation dataset which was not used during the FR modeling process was used to validate the groundwater potential map using the prediction rate method. The validation results showed that the area under the curve for frequency model is 84.78 %. As far as the performance of the FR approach is concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative groundwater potential. This information could be used by government agencies as well as private sectors as a guide for groundwater exploration and assessment in Malaysia.     

An Improved Method Based on Shallow Ice Approximation to Calculate Ice Thickness along Flow-Line and Volume of Mountain Glaciers

To evaluate the water storage and project the future evolution of glaciers, the ice-thickness of glaciers is an essential input. However, direct measurements of ice thickness are labo-rious, not feasible everywhere, and necessarily restricted to a small number of glaciers. In this article, we develop a simple method to estimate the ice-thickness along flow-line of mountain glaciers. Different from the traditional method based on shallow ice approximation (SIA), which gives a relationship be-tween ice thickn...     

Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network

A landslide is one of the natural disasters that occur in Malaysia. In addition to the geological factor and the rain as triggering factor, topographic factors such as elevation, slope angle, slope aspect, and curvature are considered as the main causes of landslides. The study in this paper was conducted in three stages. The first stage involved the extraction of extra topographic factors. Previous landslide studies had identified only four of the topographic factors. However, eight new additional factors have also been identified in this study. They are general curvature, longitudinal curvature, tangential curvature, cross-section curvature, surface area, diagonal line length, surface roughness, and rugosity. At this stage, 13 factors were extracted from the digital elevation model. The second stage involved specifying the importance of each factor. The multilayer perceptron network and backpropagation algorithm were used to specify the weight of each factor. Results were verified using the receiver operating characteristics based on the area under the curve method in the third stage. The results indicated 76.07 % accuracy in predicting of landslides, with slope angle as the most important factor while the tangential curvature has the least importance.     

Particle tracking analysis of river–aquifer interaction via bank infiltration techniques

Induced bank infiltration (BI) is commonly implemented in other countries, but remains new and unexplored in Malaysia. Increasing river pollution could affect drinking water resources. Given the threat of pollution to raw water sources, applying induced BI to sustain water management is essential. This paper presents a case study of the BI method, which evaluates the effects of groundwater pumping and BI operation on the installation of wells as well as determines the effect of pumping rate on flow paths, travel time, the size of the pumping and capture zone delineation, and groundwater mixing in a pumping well in Jenderam Hilir, Malaysia. The proposed method performs infiltration safely and achieves the ideal pumping rate. Numerical modeling packages, MODFLOW and MODPATH (particle tracking) were used. Results indicate that the migration of river water into the aquifer is generally slow and depends on the pumping rate and distance from well to the river. Most water arrives at the well by the end of a pumping period of 1–5 days at 3,072 m³/day for test wells DW1 and DW2, and during simultaneous pumping for DW2 and PW1 for a well located 36 and 18 m, respectively, from the river. During the 9.7-day pumping period, 33 % of the water pumped from the DW1 well was river water, and 38 % from DW2 throughout 4.6 days was river water. The models provide necessary information for water operators in the design and construction of pumping and sampling schedules of BI practices.     

Applying the scores of multivariate statistical analyses to characterize the relationships between the hydrochemical properties and groundwater conditions in respect of the monsoon variation in Kapas Island,Terengganu, Malaysia

The different factors (seasonal changes) and variables (physicochemical) controlling the groundwater hydrochemistry of Kapas Island were identified using multivariate techniques principal component analysis (PCA), discriminant analysis (DA) and hierarchy cluster analysis (HCA). In the present study, the hydrochemistry of 216 groundwater samples, consisting of information concerning the in situ parameters and major ions in six monitoring boreholes, was studied and compared in two different monsoon seasons. The dominant variables derived from four components by PCA in the pre-monsoon indicated the influence of the salinity process, while the dominant variables derived from three components in the post-monsoon mostly indicated on the mineralization process. The DA gave the final variables after discriminating the insignificant variables based on the pre- and post-monsoon classifications. This provided important data reduction in terms of the mineralization process, as it only discriminated physical variables (TDS, EC, salinity, DO and temperature). Based on the HCA result, samples belonging to stations KW 3 and KW 4 were under Ca-rich water, while the remaining boreholes were grouped in Na-rich water.     

Study of the climatic teleconnection between the Siberian high and maritime continent warm pool

This paper attempts to establish a method for analysing the relationship between the polar and equatorial climate of the Northern Hemisphere. The Arctic Oscillation （AO） is known to have no direct relationship with the monsoon over the Maritime Continent （MC）. Thus, an index called the Siberian High（SH~Maritime Continent（MC） Index （SHMCI） is developed to represent the mean sea level pressure difference between the SH and the warm pool over the MC. This index indicates a strong link with the monsoon circulation. A positive （strong） value of the SHMCI is associated with strong meridional winds and intense and frequent cold surge events over the South China Sea. The correlation between the AO index and the SHMCI is -0.39, which is medium but statistically significant; however, it is not sufficiently conclusive to infer direct correlation. Nevertheless, the SHMCI can be used as a tool to relate the AO with the monsoon over the MC because of the influence demonstrated by the AO towards the SH. Further analysis on the convergence and divergence anomalies over the MC reveals an impact discernible only from the SHMCI. This implies that the SHMCI manifests clearly the relationship between the Arctic and equatorial climate.     

Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction

Wind velocity assumes a critical part for measuring the power created by the wind turbines. Nonetheless, power production from wind has a few weaknesses. One significant issue is that wind is a discontinuous energy source which implies that there exists substantial variability in the generation of vigor because of different variables, for example, wind speed. Wind direction is a significant variable for proficient turbine control for getting the most energy with a given wind speed. Taking into account the conjectures on wind heading, it might be conceivable to adjust the turbine to the wind bearing to get the most energy yield. Since both forecasts of wind speed and direction are basic for effective wind energy collecting it is crucial to develop a methodology for estimation of wind speed and direction and afterwards to estimate wind farm power production as function of wind pace and heading distribution. Despite the fact that various numerical functions have been proposed for demonstrating the wind speed and direction frequency distribution, there are still disadvantages of the models like very demanding in terms of calculation time. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a particular sort of the artificial neural networks (ANN) family, was used to anticipate the wind speed and direction frequency dispersion. Thereafter, the ANFIS system was utilized to gauge wind homestead power creation as function of wind velocity and bearing. Neural system in ANFIS modifies parameters of enrollment capacity in the fuzzy logic of the fuzzy inference system. The reenactment outcomes exhibited in this paper demonstrate the adequacy of the created technique.