Lamine Diop

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Trend analysis of streamflow with different time scales: a case study of the upper Senegal River

Abstract This study investigates long-term trends of three different time scales including monthly, seasonally and annually at the upper Senegal River basin. Daily streamflows for the period 1961–2014 at Bafing Makana station were used and analyzed to conduct this research. The serial structural of the different time series (monthly, seasonal, and annual) were investigated in order to detect the presence of autocorrelation. Mann–Kendall test was applied to no autocorrelated series and the Modified Mann–Kendall test for the autocorrelated. Theil and Sen’s slope estimator test was used for finding the magnitude of change and Pettitt test was applied for detecting the most probable change year. Results exhibited a decreasing trend of the annual streamflow yet at the 5% significance level, streamflow series did not have any statistically significant trend for the whole period; however, integrating the different change years, decreasing trend is significant before the first breaking point (1976) and increasing trend is significant from first breaking point to the second change point (1993). For the monthly series, all months exhibit a non-significant decreasing trend except for the month of June. The seasonal series show a decreasing trend which a significant at MAMJ season. Change years were varying accordantly to the scale.

Evaluation of Valiantzas’ Simplified Forms of the FAO-56 Penman-Monteith Reference Evapotranspiration Model in a Humid Climate

AbstractThe unavailability of some meteorological variables, especially solar radiation and wind speed, is the main constraint for reference evapotranspiration (ETo) estimation using the standard U...

Evaluation of the Penman-Monteith and other 34 reference evapotranspiration equations under limited data in a semiarid dry climate

Reference evapotranspiration (ETo) is an important parameter in hydrological, agricultural, and environmental studies. Accurate estimation of ETo helps to improve water management and increase water productivity and efficiency. While the Penman-Monteith ETo equation enjoys worldwide adoption as the most accurate ETo equation, the number of requested climatic variables makes its application very questionable under limited data conditions. The objective of this study was to evaluate the Penman-Monteith ETo equation under limited climatic data and 34 simple ETo equations that request few climatic variables. Five weather stations were considered under the semiarid and dry climate across New Mexico for the period of 2009–2017. The Penman-Monteith ETo equation showed good performance under missing solar radiation, relative humidity, and wind speed and could still be adapted under limited data conditions across New Mexico. However, it tended to underestimate daily ETo when more than one climatic variable data is missing. Among the simple ETo equations, four of the Valiantzas equations, along with the Makkink, Calibrated Hargreaves, Abtew, Jensen-Haise, and Caprio equations, were the best performing ones compared to the Penman-Monteith equation and could be the best alternative ETo estimation methods. These alternative equations could be used by irrigation managers, producers, engineers, and university researchers to improve water management across the dry semiarid and arid zone across New Mexico, as well as other semiarid areas where water is the most limiting factor to food and fiber production.