تصور معذوریت قرآن و سنت کی روشنی میں

The action code of Islam is impartial and strong. All human beings, disabled and abled have the right of gaining justice and bound to giving justice. Because of disableness impartial behavior is against the Islamic justice code. The “good behavior” with disabled persons is the basic law of disableness in Islam. The “good behavior” with disabled persons in Islam is not the result of mercy and pity but is the result of that right of taking equal to the life of common people which is due to the nation and community. Hence the rights of disabled persons are the result of those requirements which are created due to disableness. The meaning which we take conventional is “complete worthlessness” but it means in Islam weak and feeble. That is to say disable person is able to work but he has less ability. Or he has no ability to do one job but has the ability of high rank to do other jobs. The Holy Prophet (Peace be upon him) introduced first time this principle of disableness. The Arabs mean the word “disableness” complete uselessness. But the Holy Quran exempted the persons from Jehad not taking part in Tabuk battle because of disableness giving them the name of feebles. Islam does not appeal for mercy with disabled persons but advised to behave well with them and condemns also the injustice of society with them. Islam orders to perform one’s duties to others. Islam not only stresses on the performance of duties but also gives instructions in this connection.

Data Driven Modelling for Improved Water Management in Indus Basin

Proper water resources planning, development, and management need reliable forecasts of river flows. The trends of two hydrologic variables including precipitation and temperature and their effects on streamflow have been examined at the start of this thesis. Thirty years’ (1985 – 2014) data from eight climatic stations located in five subbasins (Skardu, Gupis, Gilgit, Drosh, and Astore) of the Upper Indus River Basin (UIRB) have been analysed. The climate station data were compared with the results of two General Circulation Models/ Global Climate Model (GCMs), BCC-CSM1-1and GFDL-CM3 (each with RCP 2.6 and RCP 8.5 scenario), in order to check their commonalities and differences. The statistical properties of the selected variables and their diversities linked with the characteristics of the UIRB were estimated using various stochastic techniques. The variation in the streamflow of Astore River, a tributary of Indus River, due to the impact of the changing trends of the two variables temperature and precipitation was assessed. The escalating temperature in three of the four seasons, as well as the increase in precipitation in the summer and spring seasons, will evidently result in longer summers and shorter winters. It will also produce an increasing runoff in the basin annually on a short-term basis whereas the runoff will decline in the distant future. In recent decades an important technique has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence based modeling with several categories of models. In this thesis, the performance of three Artificial Neural Network (ANN) and four Support Vector Regression (SVR) models have been investigated to predict streamflow of the Astore River. Results from ANN models using three different optimization techniques namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation were compared with one another. A further comparison was made between these ANN and four types of SVR models which were based on linear, polynomial, radial basis function, and the sigmoid kernels. Three types of input combinations with main input variables (temperature, precipitation, and streamflow) and several types of combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using Correlation Coefficient Analysis, Monte Carlo Analysis (MCA) and Genetic Algorithm. The performance of the ANN and SVR models was evaluated by mean bias error and Nash-Sutcliffe efficiency. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon -ANN model was found to be much better than that of the other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than that of the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN and Global Climate Model GCM. It was found that the stream flow of the selected river has increasing trends till mid-21stcentury and decreasing trend by the last decade of the century and even onwards. The result of GCMs reported values under the RCP 2.6 and RCP 8.5 scenarios showed almost the same pattern in the trends of the streamflow throughout the century with higher stream-flows predicted for RCP 8.5 scenario. Although observed data was used to test the data-driven models, this thesis also compares a Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) conceptual model and the ANN model coupled with conjugate gradient (CG) method to predict the streamflow. The results show that the hybrid ANN-CG model can predict streamflow very close to HEC-HMS. A parametric study was undertaken using MCA and found that the most important parameters for HEC-HMS models are the storage coefficient (S) and the time of concentration (tc); for ANN models, input combinations are the most important, which were determined by application of MCA to ANN first time. This study measures the uncertainty allied with these parameters and the outcomes that can be used to confine the range at which preliminary estimations are made in future modeling. Finally, the impact of any errors in streamflow predictions on flowduration curves (FDC) has been investigated. It is noticed that the FDCs are significantly affected by any inaccuracy in simulating the streamflow. The FDC evaluated that extreme event (floods and low flows) are expected in the selected river basin in near and distant future. All the above techniques applied to predict the streamflow in UIB shows that there will be an increase in the water availability in the short term but the streamflow will decrease in the long term. Any changes in the streamflow will obviously change the level of water in reservoirs downstream of study area, especially the Tarbela reservoir located on the downstream of UIRB, which will require changing the reservoir operating policy for better management of available water. This thesis has provided comprehensive data for current and future sustainable water resources management within the basin. Keywords: Water resource management, Artificial Neural Network, Climate change, CUSUM test, Flow Duration Curves, Genetic Algorithm, HEC-HMS, Mann-Kendall test, Monte Carlo Analysis, River Indus, Rank sum test, Sen’s slope test, Short-term Streamflow Forecast, Support Vector Machine, Sensitivity Analysis, Tarbela, Trend analysis.