Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future.

Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing.

A novel stochastic wastewater quality modeling based on fuzzy techniques.

Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM).

A generalized linear stochastic model for lake level prediction.

Endorheic lakes are one of the most important factors of an environment. Regarding their morphology, these lakes, in particular saline lakes, are much more sensitive and can either benefit or pose a threat to their surroundings. Thus, constant monitoring of such lakes' water level, modeling and analyzing them for future planning and management policies is vitally important.

Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology.

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM).

Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate.

A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case.

Predicting fruit and vegetable processing wash-water quality.

Wastewaters from the fresh produce processing industry are high in solids and organic matter requiring adequate treatment prior to disposal or recycling. Characterization of the processing wastewater, also referred to as wash-water is challenging, as the quality is a function of the produce. Analysis of water quality parameters, such as total suspended solids, total solids, total dissolved solids, chemical oxygen demand, biochemical oxygen demand, total nitrogen, total phosphorus, ammonia, and electrical conductivity from different fruit and vegetable operations were analyzed to develop the innovative power function models and ranking system to estimate wash-water quality.

Optimizing best management practices to control anthropogenic sources of atmospheric phosphorus deposition to inland lakes.

Unlabelled: Excessive phosphorus loading to inland freshwater lakes around the globe has resulted in nuisance plant growth along the waterfronts, degraded habitat for cold-water fisheries, and impaired beaches, marinas, and waterfront property. The direct atmospheric deposition of phosphorus can be a significant contributing source to inland lakes. The atmospheric deposition monitoring program for Lake Simcoe, Ontario, indicates roughly 20% of the annual total phosphorus load (2010-2014 period) is due to direct atmospheric deposition (both wet and dry deposition) on the lake.

Forecasting air quality time series using deep learning.

Unlabelled: This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM).

Enhanced roadside drainage system for environmentally sensitive areas.

Stormwater runoff from roadways that encroach upon environmentally sensitive areas (ESAs) is one of the leading causes of degradation in urbanizing watersheds around the world. This is due to toxicity of the pollutant cocktail commonly found in roadway runoff, including heavy metals and sediments, as well as road salts from winter maintenance operations. This paper presents a novel design of an enhanced roadside drainage system (ERDS); an improved roadside drainage system that is intended to protect groundwater recharge zones and sensitive aquatic species in ESAs.

A novel risk assessment method for landfill slope failure: Case study application for Bhalswa Dumpsite, India.

Rapid population growth of major urban centres in many developing countries has created massive landfills with extraordinary heights and steep side-slopes, which are frequently surrounded by illegal low-income residential settlements developed too close to landfills. These extraordinary landfills are facing high risks of catastrophic failure with potentially large numbers of fatalities. This study presents a novel method for risk assessment of landfill slope failure, using probabilistic analysis of potential failure scenarios and associated fatalities.

Mapping air quality zones for coastal urban centers.

Unlabelled: This study presents a new method that incorporates modern air dispersion models allowing local terrain and land-sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year's worth of 1-hr prognostic weather data.

Evaluation of air quality zone classification methods based on ambient air concentration exposure.

Unlabelled: Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure.

Berm design to reduce risks of catastrophic slope failures at solid waste disposal sites.

Existing waste disposal sites are being strained by exceeding their volumetric capacities because of exponentially increasing rates of municipal solid waste generation worldwide, especially in densely populated metropolises. Over the past 40 years, six well-documented and analyzed disposal sites experienced catastrophic failure. This research presents a novel analysis and design method for implementation of a series of in-situ earth berms to slow down the movement of waste material flow following a catastrophic failure.