The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin's hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literature for flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies-specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study's findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

The Space Applications Centre (SAC), one of the major centers of the Indian Space Research Organization (ISRO), is developing a high resolution, dual-frequency Synthetic Aperture Radar as a science payload on Chandrayaan-2, ISRO's second moon mission. With this instrument, ISRO aims to further the ongoing studies of the data from S-band MiniSAR onboard Chandrayaan-1 (India) and the MiniRF of Lunar Reconnaissance Orbiter (USA). The SAR instrument has been configured to operate with both L- and S-bands, sharing a common antenna. The S-band SAR will provide continuity to the MiniSAR data, whereas L-band is expected to provide deeper penetration of the lunar regolith. The system will have a selectable slant-range resolution from 2 m to 75 m, along with standalone (L or S) and simultaneous (L and S) modes of imaging. Various features of the instrument like hybrid and full-polarimetry, a wide range of imaging incidence angles (similar to 10 degrees to similar to 35 degrees) and the high spatial resolution will greatly enhance our understanding of surface properties especially in the polar regions of the Moon. The system will also help in resolving some of the ambiguities in interpreting high values of Circular Polarization Ratio (CPR) observed in MiniSAR data. The added information from full-polarimetric data will allow greater confidence in the results derived particularly in detecting the presence (and estimating the quantity) of water ice in the polar craters. Being a planetary mission, the L&S-band SAR for Chandrayaan-2 faced stringent limits on mass, power and data rate (15 kg, 100 W and 160 Mbps respectively), irrespective of any of the planned modes of operation. This necessitated large-scale miniaturization, extensive use of on-board processing, and devices and techniques to conserve power. This paper discusses the scientific objectives which drive the requirement of a lunar SAR mission and presents the configuration of the instrument, along with a description of a number of features of the system, designed to meet the science goals with optimum resources. (C) 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.