Drought is a frequent occurrence in semidesert areas of southern Ethiopia that significantly affect regional, social, economic, and environmental conditions. Lack of rainfall monitoring network, instrument measurement, and failure are major bottlenecks for agro-and hydroclimate research in developing countries. The objectives of this study were to evaluate the performance of CHIRPS rainfall product and to assess meteorological drought using SPI for the period 2000 to 2020 over Gamo Zone, southern Ethiopia. The performance of CHIRPS v2 was assessed and compared to station observations (2000–2020) in the study domain to derive SPI on a three-month timescale. The Pearson correlation coefficient (R), bias, probability of bias (PBias), mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Nash simulation efficiency (NSE) values across the zone for CHIRPS v2 were found to be 0.88, 1.02, 2.56, 0.25, 22.41, 33.14, and 0.77, respectively. The results indicate that CHIRPS performed good ability to analyze the drought characteristics in the Gamo Zone. The spatial and temporal distribution method of meteorological drought has been evaluated using the Climate Data Tool (CDT). The Standardized Precipitation Index (SPI) was computed using the gamma distribution method. The magnitude of (SPI-3) of monthly and seasonal (MAM) meteorological drought in the zone from 2000 to 2020. The result shows that the known historic drought years (2014, 2015, 2010, 2009, and 2008) were indicated very well. Furthermore, sever and extreme droughts were observed in 2008 and 2009 with drought duration of 6.7 and 6.3, respectively, in most areas of the zone. Hence, this study revealed that CHIRPS can be a useful supplement for measuring rainfall data to estimate rainfall and drought monitoring in this region.

In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach.

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m² at the minimum and 0.57 kg/m² at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R² = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO⁻₃-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (T_on), wire speed (W_s), voltage , and pulse-off time (T_off) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings.