Nadeem Nawaz

Assessment of groundwater potential zones in an arid region based on catastrophe theory

Evaluation of groundwater potential is a multi-criteria and multi-level comprehensive assessment system that needs judgment of decision makers in making decision. To avoid subjectivity or the preference of decision makers in the assessment, catastrophe theory based evaluation method is proposed in this study which calculates the importance of one criterion over other by its inner mechanism and thus, avoid subjectivity. The proposed method is applied for the assessment of groundwater potential zones in the arid region of lower Balochistan province of Pakistan. The groundwater is considered as a system with five sub-systems namely, geology, soil, drainage density, slope and rainfall. Seventeen sub-system indicators of groundwater potential are selected for modeling groundwater potential zone. The catastrophe theory is applied to derive the relative weights of indicators in predicting groundwater potential. Thematic maps of sub-systems are integrated within a geographical information system and the groundwater potential zones of the integrated layer are calculated by using the weights of indicators. The results are verified by existing number of tube wells operating in the study area. It has been found that the number of tube wells is more in the area where the groundwater potential is high. The study reveals that catastrophe theory is suitable for assessing groundwater potential.

Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) for River Stage Prediction in a Tropical Catchment

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach

The uncertainties in climate projections in arid regions are quite high due to the large variability of climate and the lack of high-quality climate observations. In this study, an ensemble of four Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Model (GCM) namely GISS-E2-H, HadGEM2-ES, MIROC5, and NorESM1-M simulations was downscaled for the assessment of the spatiotemporal changes in precipitation in the data-scarce arid province (Balochistan) of Pakistan for four Representative Concentration Pathway (RCP) scenarios. The gauge-based gridded precipitation data of the Global Precipitation Climatology Centre (GPCC) having a spatial resolution of 0.5° was used for this purpose. Support Vector Machine (SVM) was used for the development of non-local model output statistics (MOS) downscaling models for each grid by linking the GPCC precipitation with the GCM simulated precipitation across a spatial domain (latitudes 03°–45° N and longitudes 42°–92° E). Then, Random Forest (RF) algorithm was used to develop the multi-model ensemble (MME) of downscaled precipitation projections. The performances of the models were assessed in terms of normalized root mean square error (NRMSE), percentage of bias (PBIAS), and modified index of agreement (md). The results indicated that the non-local SVM-based MOS models coupled with RF MME can simulate historical precipitation over the region quite well. The MME of GCMs projected changes in the annual, monsoon, and winter precipitation in the range of − 30% to 30% for different RCPs. Overall, the MME of GCMs indicated an increase in precipitation in the monsoon-dominated wetter regions in the east, while a decrease in winter precipitation dominated arid region in the west. A decrease in annual precipitation over the majority of the southeast, east, and northeastern arid regions was projected which may increase the aridity in the region.